Package 'cluster.datasets'

Description

The table contains measures of various compounds in cebrospinal fluid and blook for acidosis patients. This is Table 14.11 in Chapter 14 of Hartigan (1975) on page 265.

Usage

data(acidosis.patients)

Format

A data frame with 40 observations on the following 6 variables.

ph.cerebrospinal.fluid

a numeric vector

ph.blood

a numeric vector

hco3.cerebrospinal.fluid

a numeric vector

hco3.blood

a numeric vector

co2.cerebrospinal.fluid

a numeric vector

co2.blood

a numeric vector

Details

Hartigan suggests the use of the direct splitting algorithm with this data set.

Source

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(acidosis.patients)

Description

The table contains the airline distances in hunds of miles between the principal cities of the world. This is Table 11.1 in Chapter 11 of Hartigan (1975) on page 192.

Usage

data(airline.distances.1966)

Format

A data frame with 30 observations on the following 31 variables.

code

a character vector for the cities

AZ

a numeric vector for Azores

BD

a numeric vector for Baghdad

BN

a numeric vector for Berlin

BY

a numeric vector for Bombay

BS

a numeric vector for Buenos Aires

CO

a numeric vector for Cairo

CN

a numeric vector for Capetown

CH

a numeric vector for Chicago

GM

a numeric vector for Guam

HU

a numeric vector for Honolulu

IL

a numeric vector for Istanbul

JU

a numeric vector for Juneau

LN

a numeric vector for London

MA

a numeric vector for Manila

ME

a numeric vector for Melbourne

MY

a numeric vector for Mexico City

ML

a numeric vector for Montreal

MW

a numeric vector for Moscow

NS

a numeric vector for New Orleans

NY

a numeric vector for New York

PY

a numeric vector for Panama City

PS

a numeric vector for Paris

RO

a numeric vector for Rio De Janeiro

RE

a numeric vector for Rome

SF

a numeric vector for San Francisco

SO

a numeric vector for Santiago

SE

a numeric vector for Seattle

SI

a numeric vector for Shanghai

SY

a numeric vector for Sydney

TO

a numeric vector for Tokyo

Details

Hartigan uses this data set with the single linkage algorithm.

Source

The World Almanac (1966).

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(airline.distances.1966)

Description

The table contains a list of animals and the constituents of their milk. A shorter version appearsa in jh.table.1.2. This is Table 16.3 in Chapter 16 of Hartigan (1975) on page 304.

Usage

data(all.mammals.milk.1956)

Format

A data frame with 25 observations on the following 6 variables.

name

a character vector for the animal name

water

a numeric vector for the percentage of water

protein

a numeric vector for the percentage of protein

fat

a numeric vector for the percentage of fat

lactose

a numeric vector for the percentage of lactose

ash

a numeric vector for the percentage of ash.

Details

Hartigan suggests the use of a joiner-scaler algorithm on this data set.

Source

Spector, W. S. (1956) Handbook of Biological Data, Saunders.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(all.mammals.milk.1956)

Description

The table records city crime along with population statistics. This is Table 18.6 in Chapter 18 of Hartigan (1975) on page 342.

Usage

data(all.us.city.crime.1970)

Format

A data frame with 24 observations on the following 10 variables.

city

a character vector for the city name

population

a numeric vector for th epopulation in thousands

white.change

a numeric vector for the percent change in inner city white population from 1960 to 1970

black.population

a numeric vector for the black population in thousands

murder

a numeric vector for the murder rate

rape

a numeric vector for the rape rate

robbery

a numeric vector for the robbery rate

assault

a numeric vector for the assault rate

burglary

a numeric vector for the burglary rate

car.theft

a numeric vector for the car theft rate

Details

All rate variables are per 100,000 population. Hartigan suggests using the AID algorithm on this data set.

Source

The Statistical Abstract of the United States (1971), Bureau of Census, Department of Commerce, Grossett and Dunlop, New York.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(all.us.city.crime.1970)

Description

The table defines the position of amino acids for Cytochrome-c. This is Table 13.4 in Chapter 13 of Hartigan (1975) on page 240.

Usage

data(amino.accid.sequence.1972)

Format

A data frame with 17 observations on the following 37 variables.

species

a character vector for the species names

p.1

a factor for position 1 with levels I V

p.2

a factor for position 2 with levels A E

p.3

a factor for position 3 with levels I T V

p.4

a factor for position 4 with levels I T V

p.5

a factor for position 5 with levels M Q

p.6

a factor for position 6 with levels A S

p.7

a factor for position 7 with levels C V

p.8

a factor for position 8 with levels K N

p.9

a factor for position 9 with levels T V

p.10

a factor for position 10 with levels H N S W Y

p.11

a factor for position 11 with levels F I

p.12

a factor for position 12 with levels A E P Q V

p.13

a factor for position 13 with levels F Y

p.14

a factor for position 14 with levels S T

p.15

a factor for position 15 with levels A D E

p.16

a factor for position 16 with levels N S

p.17

a factor for position 17 with levels I T V

p.18

a factor for position 18 with levels G K N Q

p.19

a factor for position 19 with levels E N Q

p.20

a factor for position 20 with levels D E

p.21

a factor for position 21 with levels M R

p.22

a factor for position 22 with levels E I

p.23

a factor for position 23 with levels I V

p.24

a factor for position 24 with levels T V

p.25

a factor for position 25 with levels I L

p.26

a factor for position 26 with levels K S

p.27

a factor for position 27 with levels K

p.28

a factor for position 28 with levels A D E G K S T

p.29

a factor for position 29 with levels A E Q T V

p.30

a factor for position 30 with levels D N

p.31

a factor for position 31 with levels I V

p.32

a factor for position 32 with levels D E K Q S

p.33

a factor for position 33 with levels A K T

p.34

a factor for position 34 with levels A C T

p.35

a factor for position 35 with levels A K N S

p.36

a factor for position 36 with levels - A E K S

Details

The factor levels across the 36 positions common. Hartigan uses the reduced mutation algorithm with this data set.

Source

Dickerson, R. E. (1972). The structure and history of an ancient problem, Scientific American, 222(4), 58-72.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(amino.acid.sequence.1972)

Description

The table is a binary table that identifies which animals are in given cluster. This is Table 8.1 in Chapter 8 of Hartigan (1975) on page 155.

Usage

data(animal.cluster.trees)

Format

A data frame with 13 observations on the following 11 variables.

symbol

a character vector for

name

a character vector for

c.1

a numeric vector for a binary variable. A value 1 means the animal is in cluster 1 while 0 means that it is not in that cluster

c.2

a numeric vector for a binary variable. A value 1 means the animal is in cluster 2 while 0 means that it is not in that cluster

c.3

a numeric vector for a binary variable. A value 1 means the animal is in cluster 3 while 0 means that it is not in that cluster

c.4

a numeric vector for a binary variable. A value 1 means the animal is in cluster 4 while 0 means that it is not in that cluster

c.5

a numeric vector for a binary variable. A value 1 means the animal is in cluster 5 while 0 means that it is not in that cluster

c.6

a numeric vector for a binary variable. A value 1 means the animal is in cluster 6 while 0 means that it is not in that cluster

c.7

a numeric vector for a binary variable. A value 1 means the animal is in cluster 7 while 0 means that it is not in that cluster

c.8

a numeric vector for a binary variable. A value 1 means the animal is in cluster 8 while 0 means that it is not in that cluster

c.9

a numeric vector for a binary variable. A value 1 means the animal is in cluster 9 while 0 means that it is not in that cluster

Details

This table is used to construct and present a cluster tree as defined in Hartigan (1975).

Source

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(animal.cluster.trees)

Description

A table with birth and death rates per 1000 persons for selected countries. This is Table 11.6 in Chapter 11 of Hartigan (1975) on page 197.

Usage

data(birth.death.rates.1966)

Format

A data frame with 70 observations on the following 3 variables.

country

a character vector for the country name

birth

a numeric vector for the birth rates per 1000 persons

death

a numeric vector for the death rates per 1000 persons

Details

Hartigan recommends that spircal search algorithm be applied to this data set.

Source

Reader's Digest Almanac (1966)

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(birth.death.rates.1966)

Description

The table defines the metamorphisis sequences of British butterflies. This is Table 7.6 in Chapter 7 of Hartigan (1975) on page 150.

Usage

data(british.butterfly.appearance)

Format

A data frame with 27 observations on the following 13 variables.

name

a character vector for the species

jan

a factor for January occurrences with levels I L O P

feb

a factor for February occurrences with levels I L O P

mar

a factor for March occurrences with levels I L O P

apr

a factor for April occurrences with levels I L LP O OL P PI

may

a factor for May occurrences with levels I L LI LP LPI P PI

jun

a factor for June occurrences with levels I IL IOL L LI LP LPI P PI

jul

a factor for July occurrences with levels I L LI LP LPI O P PI

aug

a factor for August occurrences with levels I L LI LPI O P PI

sep

a factor for September occurrences with levels I L LI LP LPI O P PI

oct

a factor for October occurrences with levels I L LP LPI O P

nov

a factor for November occurrences with levels I L O P

dec

a factor for December occurrences with levels I L O P

Details

Hartigan suggests using this data set to test the ditto algorithm.

Source

Ford, T. L. E. (1963). Practical Entomology, Warne, London, p. 181.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(british.butterfly.appearance)

Description

The table identifies for each cake which ingredient is used and the quantity. This is Table 12.8 in Chapter 12 of Hartigan (1975) on page 229.

Usage

data(cake.ingredients.1961)

Format

A data frame with 18 observations on the following 35 variables.

Cake

a character vector for the name of the cake

AE

a numeric vector for the amount of Almond essence in teaspoons

BM

a numeric vector for the amount of Buttermilk in cups

BP

a numeric vector for the amount of Baking powder in teaspoons

BR

a numeric vector for the amount of Butter in cups

BS

a numeric vector for the amount of Bananas in whole bananas

CA

a numeric vector for the amount of Cocoa in tablespoons

CC

a numeric vector for the amount of Cottage Cheese in pounds

CE

a numeric vector for the amount of Chocolate in ounces

CI

a numeric vector for the amount of Crushed Ice in cups

CS

a numeric vector for the amount of Crumbs in cups

CT

a numeric vector for the amount of Cream of tartar in teaspoons

DC

a numeric vector for the amount of Dried currants in tablespoons

EG

a numeric vector for the amount of Eggs in whole eggs

EY

a numeric vector for the amount of Egg white in whole eggs

EW

a numeric vector for the amount of Egg yolk in whole eggs

FR

a numeric vector for the amount of Sifted flour in cups

GN

a numeric vector for the amount of Gelatin in tablespoons

HC

a numeric vector for the amount of Heavy cream in cups

LJ

a numeric vector for the amount of Lemon juice in tablespoons

LR

a numeric vector for the amount of Lemon rind in teaspoons

MK

a numeric vector for the amount of Milk in cups

NG

a numeric vector for the amount of Nutmeg in teaspoons

NS

a numeric vector for the amount of Nuts in cups

RM

a numeric vector for the amount of Rum in ounces

SA

a numeric vector for the amount of Soda in teaspoons

SC

a numeric vector for the amount of Sour cream in cups

SG

a numeric vector for the amount of Shortening in tablespoons

SR

a numeric vector for the amount of Granulated sugar in cups

SS

a numeric vector for the amount of Strawberries in quarts

ST

a numeric vector for the amount of Salt in teaspoons

VE

a numeric vector for the amount of Vanilla extract in teaspoons

WR

a numeric vector for the amount of Water in cups

YT

a numeric vector for the amount of Yeast in ounces

ZH

a numeric vector for the amount of Zwiebach in ounces

Details

For each cake and ingredient, the data frame contains NA if the ingredient is not required or a numeric value.

Source

Claiborn, C. (1961) The New York Times Cookbook, Harper and Row, New York.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(cake.ingredients.1961)

Description

The table contains the oxidation-fermentation patterns for a sample of species of Candida in terms of acid production. This is Table 15.1 in Chapter 15 Hartigan (2975) on page 279.

Usage

data(candida.oxidation.fermentation)

Format

A data frame with 8 observations on the following 13 variables.

name

a character vector for the species name

glucose

a factor for glucose with levels +

maltose

a factor for maltose with levels - +

sucrose

a factor for sucrose with levels - +

lactose

a factor for lactose with levels - +

galactose

a factor for galactose with levels - +

melibiose

a factor for melibiose with levels - +

cellobiose

a factor for cellobiose with levels - +

inositol

a factor for inositol with levels -

xylose

a factor for xylose with levels - +

raffinose

a factor for raffinose with levels - +

trehalose

a factor for trehalose with levels - +

dulcitol

a factor for dulcitol with levels - +

Details

A '+' level means oxidative production of acid where as a '-' level means no acide production. Hartigan suggests using direct joining on this data set.

Source

Hall, T. C., Webb, C. D> and Papageorge, C. (1972) Use of oxidation-fermentation medium in the identification of yeasts, HSMHA Report, 87, 172 - 176.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(candida.oxidation.fermentation)

Description

The table defines the hierarchy of insects classified according to cerci or tail appendages. This is Table 13.1 in Chapter 13 of Hartigan (1975) on page 234.

Usage

data(cerci.tail.presence)

Format

A data frame with 38 observations on the following 4 variables.

index

a numeric vector for the insect index

code

a character vector for the insect code

name

a character vector for the name of the index or family

parent

a numeric vector the index of the parent insect

Details

Hartigan applies the minimu mutation method to this data set.

Source

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(cerci.tail.presence)

Description

The table contains presidential votes recorded over 12 elections and for 8 counties in Connecticut. This is Table 14.13 in Chapter 14 of Hartigan (1975) on page 267.

Usage

data(ct.president.vote.1920.1964)

Format

A data frame with 36 observations on the following 10 variables.

year

a numeric vector for the election year

party

a character vector for the political party

fairfield

a numeric vector for Fiarfield county

hartford

a numeric vector for Hartford county

litchfield

a numeric vector for Litchfield county

middlesex

a numeric vector for Middlesex county

new.haven

a numeric vector for New Haven county

new.london

a numeric vector for New London county

tolland

a numeric vector for Tolland county

windham

a numeric vector for Windham county

Details

Hartigan recommend the use of the two direct splitting algorithm on this data set.

Source

Scammon, R. M. (1965) America at the Polls, University of Pittsburgh, Pittsburgh.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(ct.president.vote.1920.1964)

Description

The table contains by country the percentage of all households with various foods in house at the time of questionnaire. This is Table 15.9 in Chapter 15 of (Hartigan) on page 289.

Usage

data(european.foods)

Format

A data frame with 20 observations on the following 18 variables.

code

a character vector for the food code

name

a character vector for the food name

wg

a numeric vector for West Germany

it

a numeric vector for Italy

fr

a numeric vector for France

ns

a numeric vector for Netherlands

bm

a numeric vector for Belgium

lg

a numeric vector for Luxemburg

gb

a numeric vector for Great Britain

pl

a numeric vector for Portugal

aa

a numeric vector for Austria

sd

a numeric vector for Switzerland

sw

a numeric vector for Sweden

dk

a numeric vector for Denmark

ny

a numeric vector for Norway

fd

a numeric vector for Finland

sp

a numeric vector for Spain

id

a numeric vector for Ireland

Details

Hartigan suggests applying two way direct joining to this data set.

Source

A Survey of Europe Today, The Readers' Digest Association Ltd, London.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(european.foods)

Description

The table defines pairs of hardware objects that are most similar along with a dissimilar object. This is Table 10.1 in Chapter 10 of Hartigan (1975) on page 178.

Usage

data(hardware.triads)

Format

A data frame with 20 observations on the following 4 variables.

case

a character vector

similar.1

a factor for the first object of similar pair with levels B N P T

similar.2

a factor for the second object of similar pair with levels B F S T

odd

a factor for the different object with levels B F N P S T

Details

Six pieces of hardware were considered. Every possible set of three distinct pieces of hardware was examined, and a judgment was made about which two pieces were most similar. The results were reported by listing the closest pair with parentheses surrounding them, followed by the "odd" item. The hardware objects are identified as follows

1. "N" is a nail

2. "P" is a Phillips head screw

3. "B" is a bolt

4. "T" is a tack

5. "F" is a finishing nail

6. "S" is a screw

These data are used to test the triads algorithm.

Source

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(hardware.triads)

Description

This data frame contains the directory of data sets from Hartigan (1975) that are available in this package.

Usage

data(hartigan.datasets)

Format

A data frame with 53 observations on the following 4 variables.

table.name

a character vector with the table name

chapter

a numeric vector with the chapter containing the table

page

a numeric vector with the page on which the table appears

data.set.name

a character vector the data set name in this package

Details

Chapter number 0 is associated with the Introduction of the book.

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(hartigan.datasets)

Description

The table contains the correlations multiplied by 10000 for 22 caste groups each with 67 to 196 individuals. This is Table 17.6 in Chapter 17 of Hartigan (1975) on page 324.

Usage

data(indian.caste.measures)

Format

A data frame with 9 observations on the following 9 variables.

st

a numeric vector for the correlations with stature

sh

a numeric vector for the correlations with sitting height

nd

a numeric vector for the correlations with basal depth

nh

a numeric vector for the correlations with nasal height

hl

a numeric vector for the correlations with head length

fb

a numeric vector for the correlations with frontal breadth

bb

a numeric vector for the correlations with bizygometic breadth

hb

a numeric vector for the correlations with head breadth

nb

a numeric vector for the correlations with nasal breadth

Details

The data frame has as row names the variable names. The actual correlations are recovered by dividing the data frame by 10000. Hartigan suggests performing a factor analysis on the data set as well as performing a joining algorithm.

Source

Rao, C. R. (1948). The utilization of multiple measurements in problems of biological classification, J. Royal Stat. Soc. B, 10, 159 - 193.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(indian.caste.measures)

Description

The table contains foreign language equivalent of the names associated with the column names. This is Table 13.8 in Chapter 13 of Hartigan (1975) on page 243.

Usage

data(indo.european.languages)

Format

A data frame with 13 observations on the following 17 variables.

language

a character vector for the foreign language

all

a character vector for the foreign language equivalent

bad

a character vector for the foreign language equivalent

belly

a character vector for the foreign language equivalent

black

a character vector for the foreign language equivalent

bone

a character vector for the foreign language equivalent

day

a character vector for the foreign language equivalent

die

a character vector for the foreign language equivalent

drink

a character vector for the foreign language equivalent

ear

a character vector for the foreign language equivalent

eat

a character vector for the foreign language equivalent

egg

a character vector for the foreign language equivalent

eye

a character vector for the foreign language equivalent

father

a character vector for the foreign language equivalent

fish

a character vector for the foreign language equivalent

five

a character vector for the foreign language equivalent

foot

a character vector for the foreign language equivalent

Details

Hartigan suggest that the minimum mutation algorithm is applied to this data set.

Source

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(indo.european.languages)

Description

Table contains the number of monthly combat deaths for US troops, South Vietnamese troops, third party troops and enemy troops. This is Table 6.4 in Chapter 6 of Hartigan (1975) on page 139.

Usage

data(indochina.combat.deaths)

Format

A data frame with 72 observations on the following 5 variables.

month.year

a character vector for the year

us

a numeric vector for the number of US combat deaths

svn

a numeric vector for the number of South Vietnamese combat deaths

third

a numeric vector for the number of third party combat deaths

enemy

a numeric vector for the number of enemy combat deaths

Details

None

Source

Unclassified Statistics on Southeast Asia (1972), Department of Defense, OASD (Comptroller), Directorate for Information Operations.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(indochina.combat.deaths)

Description

The table contains the scores for the first half of the 1965 season of the Ivy League football games. This is Table 12.1 in Chapter 12 of Hartigan (1975) on page 217.

Usage

data(ivy.league.football.1965)

Format

A data frame with 40 observations on the following 4 variables.

home.team

a character vector for the home team code

opponent.team

a character vector for the opponent team code

home.score

a numeric vector for the home team score

opponent.score

a numeric vector for the opponent team score

Details

The following teams are represented in the table

Hartigan applies a joining algorithm to this data set.

Source

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(ivy.league.football.1965)

Description

A table of measurements for each piece in a jigsaw puzzle. This is Table 3.1 in Chapter 3 of Hartigan (1975) on page 76.

Usage

data(jigsaw.puzzle.measures)

Format

A data frame with 20 observations on the following 13 variables.

piece

a numeric vector for the number of the piece.

L1

a numeric vector for length of the line between the corners.

I1

a numeric vector for the maximum deviation of the line into the piece

O1

a numeric vector for the maximum deviation of the line out of the piece.

L2

a numeric vector for the length of the line between the corners

I2

a numeric vector for the maximum deviation of the line into the piece

O2

a numeric vector for the maximum deviation of the line out of the piece.

L3

a numeric vector for the length of the line between the corners.

I3

a numeric vector for the maximum deviation of the line into the piece

O3

a numeric vector for the maximum deviation of the line out of the piece.

L4

a numeric vector for the length of the line between the corners.

I4

a numeric vector for the maximum deviation of the line into the piece

O4

a numeric vector for the maximum deviation of the line out of the piece.

Details

A jigsaw puzzle comprises 20 pieces, arranged in a regular array and numbered as follows:

Each piece is roughly rectangular. The corners of the piece are called its vertices, and the sides are called its edges. The four edges of each piece are numbered consecutively, starting from the top and moving clockwise.

For each piece, three measurements were made on each of the four edges, estimating the length of the side, and the amount by which the edge cuts into or juts out of the line joining the two vertices on that side. The measurements are in hundredths of an inch.

Source

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(jigsaw.puzzle.measures)

Description

The table presents the percentage of the population who claimed to speak a language well enough to be understood. This is Table 15.10 in Chapter 15 of Hartigan (1975) on page 290.

Usage

data(languages.spoken.europe)

Format

A data frame with 16 observations on the following 13 variables.

country

a character vector for the country

finnish

a numeric vector for speakers of Finnish

swedish

a numeric vector for speakers of Swedish

danish

a numeric vector for speakers of Danish

norwegian

a numeric vector for speakers of Norwegian

english

a numeric vector for speakers of English

german

a numeric vector for speakers of German

dutch

a numeric vector for speakers of Dutch

flemish

a numeric vector for speakers of Flemish

french

a numeric vector for speakers of French

italian

a numeric vector for speakers of Italian

spanish

a numeric vector for speakers of Spanish

portuguese

a numeric vector for speakers of Portuguese

Details

Hartigan suggests the use of direct joining for this data set.

Source

A Survey of Europe Today, The Readers' Digest Association Ltd, London.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(languages.spoken.europe)

Description

The table contains the mortality rates from Leukemia recorded per million children between the ages of 0 to 14 and between 1956 and 1967. This is Table 18.1 in Chapter 15 of Hartigan (1975) on page 334.

Usage

data(leukemia.youth.mortality.1956.1957)

Format

A data frame with 18 observations on the following 13 variables.

country

a character vector for the country name

y.1956

a numeric vector for the mortality rates in 1956

y.1957

a numeric vector for the mortality rates in 1957

y.1958

a numeric vector for the mortality rates in 1958

y.1959

a numeric vector for the mortality rates in 1959

y.1960

a numeric vector for the mortality rates in 1960

y.1961

a numeric vector for the mortality rates in 1961

y.1962

a numeric vector for the mortality rates in 1962

y.1963

a numeric vector for the mortality rates in 1963

y.1964

a numeric vector for the mortality rates in 1964

y.1965

a numeric vector for the mortality rates in 1965

y.1966

a numeric vector for the mortality rates in 1966

y.1967

a numeric vector for the mortality rates in 1967

Details

Hartigan suggests using the adding algorithm on this data set to make a prediction.

Source

Spier (1972). Relationship between age of death to calendar yar of estimated maximum leukemia mortality rate, HSMHA Health Report, 87, 61 - 70.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(leukemia.youth.mortality.1956.1967)

Description

A table with remaining life expectancies for males and females of sampled ages. This is Table 4.10 in Chapter 14 of Hartigan (1975) on page 101.

Usage

data(life.expectancy.1971)

Format

A data frame with 31 observations on the following 10 variables.

country

a character vector for the country

year

a numeric vector for the year in in which the data were computed

m0

a numeric vector for the remaining life expectancies for a male of age 0

m25

a numeric vector for the remaining life expectancies for a male of age 25

m50

a numeric vector for the remaining life expectancies for a male of age 50

m75

a numeric vector for the remaining life expectancies for a male of age 75

f0

a numeric vector for the remaining life expectancies for a female of age 0

f25

a numeric vector for the remaining life expectancies for a female of age 25

f50

a character vector for the remaining life expectancies for a female of age 50

f75

a numeric vector for the remaining life expectancies for a female of age 75

Details

None.

Source

Keylitz, N. and Flieger, W. (1971), Population, Freeman.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(life.expectancy.1971)

Description

Table defines life expectancy by attained age and sex in various cities in the specified years. This is Table 10.3 in Chapter 10 of Hartigan (1975) on page 182.

Usage

data(life.expectancy.age.sex.1971)

Format

A data frame with 16 observations on the following 10 variables.

city

a character vector for the city

year

a numeric vector for the year of census

m00

a numeric vector for the male expectancy with attained age 0

m25

a numeric vector for the male expectancy with attained age 25

m50

a numeric vector for the male expectancy with attained age 50

m75

a numeric vector for the male expectancy with attained age 75

f00

a numeric vector for the female expectancy with attained age 0

f25

a numeric vector for the female expectancy with attained age 25

f50

a numeric vector for the female expectancy with attained age 50

f75

a numeric vector for the female expectancy with attained age 75

Details

This data set can be applied to the triads-leader algorithm.

Source

Keyfitz, N. and Flieger, W. (1971) Population, Freeman, San Francisco.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(life.expectancy.age.sex.1971)

Description

Frequencies with which a pair is judged more highly related than other pairs, over many triads and subjects. This is Table 10.4 in Chapter 10 of Hartigan (1975) on page 184.

Usage

data(linguistic.relatedness)

Format

A data frame with 6 observations on the following 7 variables.

word

a character vector for the

the

a numeric vector for the frequency with which words are related to 'the'

boy

a numeric vector for the frequency with which words are related to 'boy'

has

a numeric vector for the frequency with which words are related to 'has'

lost

a numeric vector for the frequency with which words are related to 'lost'

a

a numeric vector for the frequency with which words are related to 'a'

dollar

a numeric vector for the frequency with which words are related to 'dollar'

Details

This is an unusual data set to be used with the triads-leader algorithm.

Source

Levelt, W. J. M (1967). Psychological representations of syntactic structures, in The Structure and Psychology of Language, T. G. Bever and W. Weksel, eds, Holt, Rinehart and Winston, New York.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(linguistic.relatedness)

Description

The table contains for each animal the number of teeth in each major grouping. This is Table 9.1 in Chapter 9 of Hartigan (1975) on page 170.

Usage

data(mammal.dentition)

Format

A data frame with 66 observations on the following 9 variables.

name

a character vector for the name of the animal

top.i

a numeric vector for the number of top incisors

bottom.i

a numeric vector for the number of bottom incisors

top.c

a numeric vector for the number of top canines

bottom.c

a numeric vector for the number of bottom canines

top.pm

a numeric vector for the the number of top premolars

bottom.pm

a numeric vector for the number of bottom premolars

top.m

a numeric vector for the number of top molars

bottom.m

a numeric vector for the number of bottom molars

Details

Hartigan uses this table to illustrate a tree-leader algorithm.

Source

Palmer, E. I. (1957). Fieldbook of Mammals , Dutton, New York.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(mammal.dentition)

Description

Some minor planets may have been sighted more than once. In the data frame, sightings thought to be of the same planet are listed together. This is Table 1.1 in the Introduction of Hartigan (1975) on page 2.

Usage

data(minor.planets.1961)

Format

A data frame with 19 observations on the following 4 variables.

name

a character vector for the year of sighting and astronomer initials

node

a numeric vector for the angle in degrees in the earth plane at which the minor planet crosses the earth's orbit

inclination

a numeric vector for the angle in degrees between the plane of the earth's orbit and the plane of the planet's orbit

axis

a numeric vector for the maximum distance of the minor planet from the sun in astronomical units

Details

None.

Source

Elements of Minor Planets (1961), University of Cincinnati Observatory

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(minor.planets.1961)

Description

The table contains mutation distance between pairs of species. This is Table 11.12 in Chapter of Hartigan (1975) on page 209.

Usage

data(mutation.distances.1967)

Format

A data frame with 20 observations on the following 22 variables.

code

a character vector for specifies identifier

species

a character vector fir the species name

s.1

a numeric vector for distance to species 1

s.2

a numeric vector for distance to species 2

s.3

a numeric vector for distance to species 3

s.4

a numeric vector for distance to species 4

s.5

a numeric vector for distance to species 5

s.6

a numeric vector for distance to species 6

s.7

a numeric vector for distance to species 7

s.8

a numeric vector for distance to species 8

s.9

a numeric vector for distance to species 9

s.10

a numeric vector for distance to species 10

s.11

a numeric vector for distance to species 11

s.12

a numeric vector for distance to species 12

s.13

a numeric vector for distance to species 13

s.14

a numeric vector for distance to species 14

s.15

a numeric vector for distance to species 15

s.16

a numeric vector for distance to species 16

s.17

a numeric vector for distance to species 17

s.18

a numeric vector for distance to species 18

s.19

a numeric vector for distance to species 19

s.20

a numeric vector for distance to species 20

Details

The distance is defined by the number of positions in the protein molecule ccytochrome-c where the two species have differnt amino acides. Hartigan uses the single-linkage algorithm on this dat set.

Source

Fitch and Margoliash (1967) Science

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(mutation.distances.1967)

Description

The table contains the attributes for a sample of nails and screws. This is Table 12.7 in Chapter 12 of Hartigan (1975) on page 228.

Usage

data(nails.screws)

Format

A data frame with 24 observations on the following 7 variables.

name

a character vector for the name of the object

threaded

a factor for the presence of threads with levels N Y

head

a factor for the type of head with levels F O R U Y

indentation

a factor for the head indentation with levels L N T

bottom

a factor for the type of bottom with levels F S

length

a numeric vector for the length in half inches

brass

a factor that determines if the object is made of brass with levels N Y

Details

All the attributes, with the exception of length, are factors. The factor values for the threaded variable are as follows.

The factor values for the head variable are as follows.

The factor values for the head indentation variable are as follows.

The value values for the brass variable are as follows

Source

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(nails.screws)

Description

The measurements are in years and months of national averages. There are ten months in the school year. At the beginning of fourth grades, the national average score is 4.0. This is Table 5.1 in Chapter 5 of Hartigan (1975) on page 118.

Usage

data(new.haven.school.scores)

Format

A data frame with 25 observations on the following 5 variables.

school

a character vector for the name of the school

reading.4

a numeric vector for the reading scores for fourth grade

arithmetic.4

a numeric vector for the arithmetic scores for fourth grade

reading.6

a numeric vector for for the reading scores for sixth grade

arithmetic.6

a numeric vector for the arithmetic scores for sixth grade

Details

None.

Source

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(new.haven.school.scores)

Description

A table with the nutrient levels in meat, fish and fowl. Nutrient levels were measured in a 3 ounce portion of various foods. This is Table 4.1 in Chapter 4 of Hartigan (1975) on page 86.

Usage

data(nutrients.meat.fish.fowl.1959)

Format

A data frame with 27 observations on the following 6 variables.

name

a character vector for the food

energy

a numeric vector for the number of calories

protein

a numeric vector for the amount of protein in grams

fat

a numeric vector for the amount of fat in grams

calcium

a numeric vector for the amount of calcium in milligrams

iron

a numeric vector for the amount of iron in milligrams

Details

None.

Source

The Yearbook of Agriculture (1959), The United States Department of Agriculture, Washington, DC.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(nutrients.meat.fish.fowl.1959)

Description

The table presents the precentage of cropland devoted to various crops in Ohio counties. This is Table 15.7 in Chapter 15 of Hartigan( 1975) on page 287.

Usage

data(ohiio.croplands.1949)

Format

A data frame with 15 observations on the following 8 variables.

county

a character vector for the county

corn

a numeric vector for the percentage of cropland devoted to corn

mixed

a numeric vector for the percentage of cropland devoted to mixed crop

wheat

a numeric vector for the percentage of cropland devoted to wheat

oats

a numeric vector for the percentage of cropland devoted to oats

barley

a numeric vector for the percentage of cropland devoted to varley

soy

a numeric vector for the percentage of cropland devoted to soy

hay

a numeric vector for the percentage of cropland devoted to hay

Details

Hartigan suggest the use of direct joining with this data set.

Source

U.S. Census of Agriculture, 1949.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(ohio.croplands.1949)

Description

Olympic track times, in tenths of a second, were recorded orver the years. This is Table 6.1 in Chapter 6 of Hartigan (1975) on page 131.

Usage

data(olympic.track.1896.1964)

Format

A data frame with 16 observations on the following 8 variables.

year

a character vector for the year

t.100m

a numeric vector for the winning time in the 100 m

t.200m

a numeric vector for the winning time in the 200 m

t.400m

a numeric vector for the winning time in the 400 m

t.800m

a numeric vector for the winning time in the 800 m

t.1500m

a numeric vector for the winning time in the 1500 m

t.5000m

a numeric vector for the winning time in the 5000 m

t.10000m

a numeric vector for the winning time in the 10000 m

Details

None.

Source

The World Almanac (1966), New York World-Telegram, New York,

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(olympic.track.1896.1964)

Description

The table contains the correlations between various body parts. This is Table 17.1 in Chapter 17 of Hartigan (1975) on page 314.

Usage

data(physical.measure.correlations)

Format

A data frame with 7 observations on the following 7 variables.

hl

a numeric vector for the correlations with head length

hb

a numeric vector for the correlations with head breadth

fb

a numeric vector for the correlations with face breadth

ft

a numeric vector for the correlations with foot

fm

a numeric vector for the correlations with forearm

ht

a numeric vector for the correlations with height

fl

a numeric vector for the correlations with finger length

Details

Hartigan suggests performing factor analysis on this data set to determine the minimum number of principal components. In addition, a joining algorithm can be performed on the data set. Note that the data frame has the variable names as row names. It can be used directly by the eigen function.

Source

Pearson, K. (1901). On lines and planes of closest fit to points in space. Philosophical Magazine, 559 - 572.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(physical.measure.correlations)

Description

From astonomical knowledge of 1970, a table of planetary moons was compiled. This is the bottom portion of Table 5.5 in Chapter 5 of Hartigan (1975) on page 122.

Usage

data(planet.earth.distances.1970)

Format

A data frame with 8 observations on the following 5 variables.

name

a character vector for the name of the planet

distance

a numeric vector for its distance from the sun in thousands of miles

diameter

a numeric vector for its diameter in miles

period

a numeric vector for the period of its orbit in hours

mass

a numeric vector for the mass, relative to the earth

Details

None.

Source

Moore, P. (1970). The Atlas of the Universe, Rand McNally, New York.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(planet.earth.distances.1970)

Description

From astonomical knowledge of 1970, a table of planetary moons was compiled. This is the top portion of Table 5.5 in Chapter 5 of Hartigan (1975) on page 122.

Usage

data(planets.moons.1970)

Format

A data frame with 31 observations on the following 4 variables.

planet.moon

a character vector for the planet and the number of the moon

distance

a numeric vector for the distance in thousands of miles between the moon and the planet

diameter

a numeric vector for the diameter in miles of the moon

period

a numeric vector for the period, in days, of the orbit of the moon about the plane

Details

None.

Source

Moore, P. (1970). The Atlas of the Universe, Rand McNally, New York.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(planets.moons.1970)

Description

The table contains the features in a collection of portable typewriters. This is Table 10.5 in Chapter 10 of Hartigan (1975) on page 186.

Usage

data(portable.typewriters)

Format

A data frame with 20 observations on the following 21 variables.

model

a character vector for the typewriter model

HT

a numeric vector for the height in inches

WH

a numeric vector for the width in inches

DH

a numeric vector for the depth in inches

WT

a numeric vector for the weight in pounds

PL

a numeric vector for the platen length

KS

a numeric vector for the number of keys

PE

a factor for the pica or elite type with levels 1

TA

a factor for the availability of tabulator with levels 0 1

TP

a factor for the availability of touch pressure control with levels 0 1

PR

a factor for the availability of platen release with levels 0 1

HH

a factor for the availability of horizontal half spacing with levels 0 1

VH

a factor for the availability of vertical half spacing with levels 0 1

PI

a factor for the availability of page end indicator with levels 0 1

PG

a factor for the availability of paper guide with levels 0 1

PB

a factor for the availability of paper bail with levels 0 1

PS

a factor for the availability of paper support with levels 0 1

EP

a factor for the availability of erasure plate with levels 0 1

TC

a factor for the availability of two carriage re;eases with levels 0 1

MR

a factor for the availability of margin release with levels 0 1

CL

a factor for the availability of carriage lock with levels 0 1

Details

Hartigan suggests that the triads algorithm be used with this data set. The factor variables are binary variables. If the value is 1, then the associated feature is available. If the value is 0, then the associated feature is not available.

Source

Consumers' Reports Buying Guide (1967), Consumers' Union, Mount Vernon, NY.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(portable.typewriters)

Description

A table with the nutrient levels in meat, fish and fowl. Nutrient levels were measured in a 3 ounce portion of various foods. Values are percentages of recommendated daily allowances. This is Table 4.2 in Chapter 4 of Hartigan (1975) on page 87.

Usage

data(rda.meat.fish.fowl.1959)

Format

A data frame with 27 observations on the following 6 variables.

name

a character vector for the food

energy

a numeric vector for the number of calorie

protein

a numeric vector for the amount of protein

fat

a numeric vector for the amount of fat

calcium

a numeric vector for the amount of calcium

iron

a numeric vector for the amount of iron

Details

None.

Source

The Yearbook of Agriculture (1959), The United States Department of Agriculture, Washington, DC.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(rda.meat.fish.fowl.1959)

Description

Selected animals have been clustered by similarity of percentage constituents in milk. This is Table 1.2 in the Introduction of Hartigan (1975) on page 6.

Usage

data(sample.mammals.milk.1956)

Format

A data frame with 16 observations on the following 5 variables.

name

a character vector for the name of the animals

water

a numeric vector for the water content in the milk sample

protein

a numeric vector for the amount of protein in the milk sample

fat

a numeric vector for the fat content in the milk sample

lactose

a numeric vector for the amount of lactose in the milk sample

Details

None

Source

Spector, W. S. (1956). Handbook of Biological Data, Saunders, London

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(sample.mammals.milk.1956)

Description

The table contains the dividend by average price for each year and for a sample of stocks. This is Table 11.13 in Chapter 11 of Hartigan (1975) on page 210.

Usage

data(sample.stock.yields.1959.1969)

Format

A data frame with 34 observations on the following 12 variables.

stock

a character vector for the company name

y.1959

a numeric vector for the dividend yield in 1959

y.1960

a numeric vector for the dividend yield in 1960

y.1961

a numeric vector for the dividend yield in 1961

y.1962

a numeric vector for the dividend yield in 1962

y.1963

a numeric vector for the dividend yield in 1963

y.1964

a numeric vector for the dividend yield in 1964

y.1965

a numeric vector for the dividend yield in 1965

y.1966

a numeric vector for the dividend yield in 1966

y.1967

a numeric vector for the dividend yield in 1967

y.1968

a numeric vector for the dividend yield in 1968

y.1969

a numeric vector for the dividend yield in 1969

Details

Hartigan proposes applying the single linkage algorithm to this data set.

Source

Moody's Handbook of Common Stocks/

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(sample.stock.yields.1959.1969)

Description

A list of cities and the number of crimes per 100,000 population, as of 1970. This is Table 1.1 in Chapter 1 of Hartigan (1975) on page 28.

Usage

data(sample.us.city.crime.1970)

Format

A data frame with 16 observations on the following 8 variables.

city

a character vector for the names of the cities

murder

a numeric vector for the murder rates

rape

a numeric vector for the rape rates

robbery

a numeric vector for the robbery rates

assault

a numeric vector for the assault rates

burglary

a numeric vector for the burglary rates

larceny

a numeric vector for the larceny rates

auto

a numeric vector for the auto crime rates

Details

None.

Source

United Sates Statistical Abstracts (1970).

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(sample.us.city.crime.1970)

Description

The table contains student responses to a questionnaire about a data analysis course. This is Table 12.4 in Chapter 12 of Hartigan (1975) on page 224.

Usage

data(student.questionnaire)

Format

A data frame with 31 observations on the following 10 variables.

question

a numeric vector for the question number

text

a character vector for the question text

s.1

a numeric vector for the response from student 1

s.2

a numeric vector for the response from student 2

s.3

a numeric vector for the response from student 3

s.4

a numeric vector for the response from student 4

s.5

a numeric vector for the response from student 5

s.6

a numeric vector for the response from student 6

s.7

a numeric vector for the response from student 7

s.8

a numeric vector for the response from student 8

Details

Student responses to the questionnaires are evaluated using the following scores.

Hartigan applies the adding algorithm to this data set.

Source

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(student.questionnaire)

Description

The table contains the votes for selected propositions by country in the United Nations between 1969 and 1970. This is Table 16.5 in Chapter 16 of Hartigan (1975) on page 306.

Usage

data(un.votes.1969.1970)

Format

A data frame with 23 observations on the following 11 variables.

country

a character vector for the country name

p.1

a factor for proposition 1 with levels A N Y

p.2

a factor for proposition 2 with levels A N Y

p.3

a factor for proposition 3 with levels A N Y

p.4

a factor for proposition 4 with levels A N Y

p.5

a factor for proposition 5 with levels A N Y

p.6

a factor for proposition 6 with levels A N Y

p.7

a factor for proposition 7 with levels A N Y

p.8

a factor for proposition 8 with levels A N Y

p.9

a factor for proposition 9 with levels A N Y

p.10

a factor for proposition 10 with levels A N Y

Details

The propositions that were voted on were as follows.

The factor levels are the outcomes for the proposition. Y implies yes, N is no and A is abstain..

Source

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(un.votes.1969.1970)

Description

The table contains the frequency of car repairs in 1969. Plus means above average. Minus means below average. This is Chapter 9 Table 9.4 in Chapter 9 of Hartigan (1975) on page 174.

Usage

data(us.car.repair.1969)

Format

A data frame with 33 observations on the following 14 variables.

model

a character vector for the model of the vehicle

BR

a factor for break system with levels - +

FU

a factor for fuel system with levels - +

EL

a factor for electrical with levels - +

EX

a factor for exhaust with levels - +

ST

a factor for steering with levels - +

EM

a factor for engine, mechanical with levels - +

RS

a factor for rattles and squeeks with levels - +

RA

a factor for real axle with levels - +

RU

a factor for rust with levels - +

SA

a factor for shock absorbers with levels - +

TC

a factor for transmission, clutch with levels - +

WA

a factor for wheel alignment with levels - +

OT

a factor for other with levels - +

Details

This table is used to illustrate the tree-leader algorithm.

Source

Consumer Reports Buying Guide (1969)

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(us.car.repair.1969)

Description

This table contains the Union and Confederate forces and numbers shot This is Table 5.4 in Chapter 5 Hartigan (1975) on page 121.

Usage

data(us.civil.war.battles)

Format

A data frame with 46 observations on the following 5 variables.

battle

a character vector for the battle names

union.forces

a numeric vector for the Union forces deployed

union.shot

a numeric vector for the Union soldiers shot

confederate.forces

a numeric vector for the Confederate forces deplayed

confederate.shot

a numeric vector for the Confederate soldiers shot

Details

The data are in chronological order.

Source

Livermore, T L. (1957). Numbers and Losses in the Civial War, Indiana University Press, Bloomington.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(us.civil.war.battles)

Description

The table contains the behavior of various bill sponsors in the 90th Congress. This is Table 13.7 in Chapter 13 of Hartigan (1975) on page 242.

Usage

data(us.congressional.bills)

Format

A data frame with 17 observations on the following 16 variables.

sponsor

a character vector for the congressman sponsor

b.1

a factor for the congressman behavior for bill 1 with levels 1 5 7 8

b.2

a factor for the congressman behavior for bill 2 with levels 1 5 6 7

b.3

a factor for the congressman behavior for bill 3 with levels 1 5 6 7

b.4

a factor for the congressman behavior for bill4 with levels 1 7

b.5

a factor for the congressman behavior for bill 5 with levels 1 6 7

b.6

a factor for the congressman behavior for bill 6 with levels 1 6 7

b.7

a factor for the congressman behavior for bill 7 with levels 1 6 7

b.8

a factor for the congressman behavior for bill 8 with levels 1 6 7

b.9

a factor for the congressman behavior for bill 9 with levels 1 6 9

b.10

a factor for the congressman behavior for bill 10 with levels 1 6 9

b.11

a factor for the congressman behavior for bill 11 with levels 1 6 9

b.12

a factor for the congressman behavior for bill 12 with levels 1 6 9

b.13

a factor for the congressman behavior for bill 13 with levels 1 6 9

b.14

a factor for the congressman behavior for bill 14 with levels 1 6 9

b.15

a factor for the congressman behavior for bill 15 with levels 1 6 9

Details

The bills, sponsoring congressmen and bill titles are as follows.

The behavior is represented by a factor with the following values

Source

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(us.congressional.bills)

Description

The table contains the cost and nutrient content, in percent daily allowance, of various foods reported in 1959. This is Table 8.5 in Cja[ter 8 of Hartigan (1975) on page 160.

Usage

data(us.food.cost.nutrients.1959)

Format

A data frame with 10 observations on the following 8 variables.

food

a character vector for the food name

cost

a numeric vector for the cost of serving in U.S. cents

size

a character vector for for the portion size

protein

a numeric vector for % recommended daily allowance of protein

iron

a numeric vector for for % recommended daily allowance of iron

thiamine

a numeric vector for for % recommended daily allowance of thiamine

riboflavin

a numeric vector for for % recommended daily allowance of riboflavin

niacin

a numeric vector for for % recommended daily allowance of niacin

Details

The table is used to construst trees and distances as described in Hartigan (1975).

Source

Yearbook of Agriculture (1959).

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(us.food.cost.nutrients.1959)

Description

The table defines the neighbors for each state. This is Table 11.10 in Chapter 11 of Hartigan (1975) on page 207.

Usage

data(us.links.between.states)

Format

A data frame with 50 observations on the following 11 variables.

code

a character vector for the state code

name

a character vector for the state name

neighbors

a numeric vector for the number of neighboring states

n.1

a character vector for the first neighbor

n.2

a character vector for the second neighbor

n.3

a character vector for the third neighbor

n.4

a character vector for the fourth neighbor

n.5

a character vector for the fifth neighbor

n.6

a character vector for the sixth neighbor

n.7

a character vector for the seventh neighbor

n.8

a character vector for the eighth neighbor

Details

Hartigan combines this data set with the per capita data set in Table 11.9 and applies the single linkage algorithm.

Source

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(us.links.between.states)

Description

The table contains the per capita income in the United Sates in 1964. This us Table 11.9 in Chapter 11 of Hartigan (1975) on page 206

Usage

data(us.per.capita.income.1964)

Format

A data frame with 50 observations on the following 3 variables.

code

a character vector for the state codes

name

a character vector for the state names

income

a numeric vector for the income per capita

Details

Hartigan applies density contour trees and single linkage clustering to this data set.

Source

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(us.per.capita.income.1964)

Description

The table contains the Republican percentage of the Presidential vote over 18 elections and for sourthern states. This is Table 14.1 in Chapter 14 of Hartigan (1975) on page 252.

Usage

data(us.president.vote.1900.1968)

Format

A data frame with 16 observations on the following 20 variables.

code

a character vector for the state code

state

a character vector for the state name

y.1900

a numeric vector for the Republican percentage in 1900

y.1904

a numeric vector for the Republican percentage in 1904

y.1908

a numeric vector for the Republican percentage in 1908

y.1912

a numeric vector for the Republican percentage in 1912

y.1916

a numeric vector for the Republican percentage in 1916

y.1920

a numeric vector for the Republican percentage in 1920

y.1924

a numeric vector for the Republican percentage in 1924

y.1928

a numeric vector for the Republican percentage in 1928

y.1932

a numeric vector for the Republican percentage in 1932

y.1936

a numeric vector for the Republican percentage in 1936

y.1940

a numeric vector for the Republican percentage in 1940

y.1944

a numeric vector for the Republican percentage in 1944

y.1948

a numeric vector for the Republican percentage in 1948

y.1952

a numeric vector for the Republican percentage in 1952

y.1956

a numeric vector for the Republican percentage in 1956

y.1960

a numeric vector for the Republican percentage in 1960

y.1964

a numeric vector for the Republican percentage in 1964

y.1968

a numeric vector for the Republican percentage in 1968

Details

Hartigan suggests that the direct splitting algorithm is applied to this data set.

Source

Peterson, S. (1969). A Statistical History of the American Presidential Elections, Ungar, New York

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(us.president.vote.1900.1968)

Description

The table contains the profit as a percentage of stockholder's equity for various economc sectors for the years 1959 through 1968. This is Table 14.12 in Chapter 14 of Hartigan (1975) on page 266.

Usage

data(us.sector.profitability.1959.1968)

Format

A data frame with 24 observations on the following 12 variables.

code

a character vector for the sector code

sector

a character vector for the sector name

y.1959

a numeric vector for the profits in year 1959

y.1960

a numeric vector for the profits in year 1960

y.1961

a numeric vector for the profits in year 1961

y.1962

a numeric vector for the profits in year 1962

y.1963

a numeric vector for the profits in year 1963

y.1964

a numeric vector for the profits in year 1964

y.1965

a numeric vector for the profits in year 1965

y.1966

a numeric vector for the profits in year 1966

y.1967

a numeric vector for the profits in year 1967

y.1968

a numeric vector for the profits in year 1968

Details

Hartigan suggests that the direct splitting algorithm be applied to this data set.

Source

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(us.sector.profitability.1959.1968)

Description

A table of demographic information for southern states for the period 1960 to 1965. This is Table 2.2 in Chapter 2 of Hartigan (1975) on page 59.

Usage

data(us.south.demographics.1965)

Format

A data frame with 16 observations on the following 24 variables.

state

a character vector for an abbreviation for the states

mean.altitude

a numeric vector for the mean altitude above sea level, in tens of feet

mean.temperature

a numeric vector for the mean annual temperature, in degrees Fahrenheit

mean.precipitation

a numeric vector for the mean annual precipitation, in inches

population.density

a numeric vector for the number of persons per square mile.

african.americans

a numeric vector for the percentage of African-Americans

median.age

a numeric vector for the median age in years

urban.population

a numeric vector for the percentage urban population

births

a numeric vector for the number of births per 1000 population

rural.population

a numeric vector for the percentage rural farm population

manufacturing.employment

a numeric vector for the percentage of employment in manufacturing

automobiles

a numeric vector for the number of automobiles per 100 population

telephones

a numeric vector for the number of telephones per 100 population

income

a numeric vector for the average income in hundreds of dollars

federal.revenue

a numeric vector for the federal revenue per 100 dollars of state and local revenue

lawyers

a numeric vector for the number of lawyers per 100,000 population

doctors

a character vector for the number of doctors per 100,000 population

white.infant.mortality

a numeric vector for the white infant mortality per 1000 births

school.years

a numeric vector for the school years completed, in tenths of a year

education.expense

a numeric vector for the education expenditure per pupil in tens of dollars

sound.plumbing

a numeric vector for the percentage of houses with sound plumbing.

gop.1960.president

a numeric vector for the percentage Republican vote in the 1960 presidential election

gop.1964.president

a numeric vector for the percentage Republican vote in the 1964 presidential election

gop.1962.1964.governor

a numeric vector for the percentage Republican vote in the 1962/1964 governor elections

Details

None.

Source

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(us.south.demographics.1965)

Description

The table defines vervet sleeping groups measured over a set of dates. This is Table 7.5 in Chapter 7 of Hartigan (1975) on page 149.

Usage

data(vervet.sleeping.groups)

Format

A data frame with 22 observations on the following 18 variables.

date

a character vector for the date in yy/mm/dd format

I

a factor for adult males with levels A B C D E

II

a factor for older adult males with levels A B C D

III

a factor for adult males with levels A B C D

IV

a factor for adult females with levels A B C D E F

V

a factor for juvenile males with levels A B C D F

VI

a factor for adult females with levels A B C D E

VII

a factor for young juvenile females with levels A B C D E

VIII

a factor for young juvenile females with levels A B C D E

IX

a factor for young juvenile females with levels A B C D E

X

a factor for juvenile females with levels A B C D E F G

XI

a factor for subadult females with levels A B C D E

XII

a factor for adult females with levels A B C D E

XIII

a factor with levels A B C D E F

XIV

a factor for invant male, son of IV with levels A B C D E F

XV

a factor for infant male, son of XII with levels A B C D E F

XVI

a factor for infant female from IV with levels A B C D E

XVII

a factor with levels A B C D E

Details

Hartigan suggests using this data set to test the ditto algorithm.

Source

Struhsaker, T. T. (1967). Behavior of servet monkeys and other cercopithecines, Science 156, 1197 - 1203.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(vervet.sleeping.groups)

Description

The table contains the evaluations of various wines from 1961 to 1970. This is Table 7.1 in Chapter 7 of Hartigan (1975) on page 144.

Usage

data(wine.evaluation.1961.1970)

Format

A data frame with 15 observations on the following 12 variables.

code

a character vector

name

a character vector

r.61

a factor with levels A E G

r.62

a factor with levels A G P

r.63

a factor with levels A D P

r.64

a factor with levels D E G P

r.65

a factor with levels A D G P

r.66

a factor with levels A G

r.67

a factor with levels A G

r.68

a factor with levels A D G P

r.69

a factor with levels A G

r.70

a factor with levels G

Details

Hartigan uses this data set to illustrate the ditto algorithm.

Source

Gourmet Magazine (August 1971) pp 30-33.

SPAETH2 Cluster Analysis Datasets http://people.sc.fsu.edu/~jburkardt/datasets/spaeth2/spaeth2.html

References

Hartigan, J. A. (1975). Clustering Algorithms, John Wiley, New York.

Examples

data(wine.evaluation.1961.1970)