Package 'COveR'

Title: Clustering with Overlaps
Description: Provide functions for overlaps clustering, fuzzy clustering and interval-valued data manipulation. The package implement the following algorithms: OKM (Overlapping Kmeans) from Cleuziou, G. (2007) <doi:10.1109/icpr.2008.4761079> ; NEOKM (Non-exhaustive overlapping Kmeans) from Whang, J. J., Dhillon, I. S., and Gleich, D. F. (2015) <doi:10.1137/1.9781611974010.105> ; Fuzzy Cmeans from Bezdek, J. C. (1981) <doi:10.1007/978-1-4757-0450-1> ; Fuzzy I-Cmeans from de A.T. De Carvalho, F. (2005) <doi:10.1016/j.patrec.2006.08.014>.
Authors: Guillaume Cleuziou [aut], Nicolas Hiot [cre]
Maintainer: Nicolas Hiot <[email protected]>
License: GPL (>= 2)
Version: 1.0.4
Built: 2024-10-22 07:20:19 UTC
Source: CRAN

Help Index

Converts an interval object to an array representation.

Description

Converts an interval object to an array representation.

Usage

## S3 method for class 'interval'
as.array(x, ...)

Arguments

x

An interval object to be converted.
...

Additional arguments to be passed to as.array().

Value

An array representation of the interval.

Examples

as.array(inter_city)

Converts an interval object to a data frame representation.

Description

Converts an interval object to a data frame representation.

Usage

## S3 method for class 'interval'
as.data.frame(x, ...)

Arguments

x

An interval object to be converted.
...

dditional arguments to be passed to as.data.frame().

Value

A data frame representation of the interval.

Examples

as.data.frame(inter_city)

A generic function to convert various R objects into interval objects.

Description

A generic function to convert various R objects into interval objects.

Usage

as.interval(x)

Arguments

x

An R object to be converted to an interval.

Value

An interval object constructed from the R object or NULL if the type is not supported.

Converts an array to an interval object.

Description

The array must have three dimensions, with the second dimension of size 2, representing the minimum and maximum values.

Usage

## S3 method for class 'array'
as.interval(x)

Arguments

x

An array to be converted to an interval object.

Value

An interval object constructed from the array if it meets the requirements, otherwise attempts to convert it to a matrix first.

Examples

as.interval(array(1:12, dim = c(2, 2, 3)))

Provides a default method for converting unsupported data types to interval.

Description

Provides a default method for converting unsupported data types to interval.

Usage

## Default S3 method:
as.interval(x)

Arguments

x

An object that does not have a supported conversion method.

Value

'NULL', indicating no conversion is possible.

Identity Conversion for Interval

Description

Identity Conversion for Interval

Usage

## S3 method for class 'interval'
as.interval(x)

Arguments

x

An interval object.

Value

The input interval object, without modification.

Examples

as.interval(inter_city)

Converts a matrix to an interval object.

Description

The number of columns in the matrix must be even, representing pairs of minimum and maximum values.

Usage

## S3 method for class 'matrix'
as.interval(x)

Arguments

x

A matrix where each pair of columns represents the minimum and maximum bounds of intervals.

Value

An interval object constructed from the matrix.

Examples

as.interval(matrix(1:12, 3, 4))

Converts a numeric vector to an interval object.

Description

The length of the numeric vector must be even, representing pairs of minimum and maximum values.

Usage

## S3 method for class 'numeric'
as.interval(x)

Arguments

x

A numeric vector where each consecutive pair of values represents an interval.

Value

An interval object constructed from the numeric vector.

Examples

as.interval(1:6)

Converts an interval object to a matrix representation.

Description

Each interval is expanded into its minimum and maximum bounds.

Usage

## S3 method for class 'interval'
as.matrix(x, ...)

Arguments

x

An interval object to be converted.
...

Additional arguments to be passed to as.vector().

Value

A matrix representation of the interval, with two columns for each interval's minimum and maximum values.

Examples

as.matrix(inter_city)

Converts an interval object to its vector representation.

Description

Converts an interval object to its vector representation.

Usage

## S3 method for class 'interval'
as.vector(x, ...)

Arguments

x

An interval object to be converted.
...

Additional arguments to be passed to as.vector().

Value

A numeric vector where each consecutive pair of values represents an interval.

Examples

as.vector(inter_city)

Generate Colors for Clustering Visualization

Description

Generates a color for each data point based on its clustering assignment, facilitating visual distinction of clusters in plots.

Usage

cluster_color(x)

Arguments

x

A clustering vector or a matrix. If a vector is provided, it represents the cluster assignments for each data point. If a matrix is provided, each row should represent a data point's membership across multiple clusters.

Value

A character vector of colors (in hexadecimal format) corresponding to the clustering assignments, suitable for use in plotting functions.

Examples

plot(iris[, 1:2], col = cluster_color(neokm(iris, 2, 0.2, 0.05)$cluster))

Transforms a matrix of membership degrees into a logical matrix based on a specified threshold.

Description

Transforms a matrix of membership degrees into a logical matrix based on a specified threshold.

Usage

degree2logical(x, t = min(apply(x, 1, max)))

Arguments

x

A matrix of membership degrees.
t

Threshold value for converting the degrees to logical values. By default, it uses the minimum of the maximum values in each row.

Value

A logical matrix where each element is 'TRUE' if it meets or exceeds the threshold, and 'FALSE' otherwise.

Examples

degrees <- matrix(runif(9), nrow = 3)
degree2logical(degrees, t = 0.5)

Performs fuzzy c-means clustering on interval data, allowing for soft clustering of data points into multiple clusters.

Description

Performs fuzzy c-means clustering on interval data, allowing for soft clustering of data points into multiple clusters.

Usage

fuzzy_icmeans(
  x,
  centers,
  m = 2,
  nstart = 2,
  distance = "euclid",
  trace = FALSE,
  iter.max = 40
)

Arguments

x

A 3D interval array representing the data to be clustered.
centers

Either the number of clusters or a set of pre-initialized cluster centers. If a number is provided, it specifies how many clusters to create.
m

A number greater than 1 that controls the degree of fuzziness in the clustering process (default is 2).
nstart

Number of times to run the clustering algorithm with different starting values to find the best solution (default is 2).
distance

A string specifying the distance metric to use, either 'euclid' for Euclidean distance or 'hausdorff' for Hausdorff distance (default is 'euclid').
trace

Logical, if 'TRUE', tracing information on the progress of the algorithm is displayed (default is 'FALSE').
iter.max

Maximum number of iterations allowed for the clustering algorithm (default is 40).

Value

A list of clustering results, including: - 'cluster': The membership matrix indicating the degree of belonging of each data point to each cluster. - 'centers': The final cluster centers. - 'totss': Total sum of squares. - 'withinss': Within-cluster sum of squares by cluster. - 'tot.withinss': Total within-cluster sum of squares. - 'betweenss': Between-cluster sum of squares. - 'size': Sizes of each cluster. - 'iter': Number of iterations run by the algorithm. - 'overlaps': The average overlap among clusters.

Examples

fuzzy_icmeans(iaggregate(iris, col = 5), 2)
fuzzy_icmeans(iaggregate(iris, col = 5), iaggregate(iris, col = 5))

Aggregates data into a 3D interval array based on a specified column.

Description

Aggregates data into a 3D interval array based on a specified column.

Usage

iaggregate(data, col = 1)

Arguments

data

The data frame to aggregate.
col

The index of the column to aggregate by.

Value

A structured interval object representing the aggregated data.

Examples

iaggregate(iris, col = 5)
iaggregate(rock, col = 4)
iaggregate(cars, col = 1)

Combines multiple interval objects into a single interval object.

Description

Combines multiple interval objects into a single interval object.

Usage

ibind(..., class = FALSE)

Arguments

...

Interval objects to bind together.
class

Logical value indicating whether to assign a new class label to each interval object when binding. If 'TRUE', each set of intervals will have a distinct class label.

Value

A new interval object containing the combined intervals from the input objects.

Examples

ibind(iaggregate(iris, 5), iaggregate(iris, 5))
ibind(iaggregate(iris, 5), iaggregate(iris, 5), iaggregate(iris, 5),
class = TRUE)

Creates intervals from Normal Distribution using specified mean and standard deviation values for both the center and half-size of the intervals.

Description

Creates intervals from Normal Distribution using specified mean and standard deviation values for both the center and half-size of the intervals.

Usage

igenerate(n, ...)

Arguments

n

Number of intervals to generate.
...

Vectors representing parameters for generating intervals: each vector should contain four values ('center mean', 'center sd', 'half-size mean', 'half-size sd').

Value

An interval object containing the generated intervals.

Examples

igenerate(1, c(0, 1, 2, 1))
igenerate(1, c(0, 1, 2, 1), c(100, 1, 2, 1))

Performs k-means clustering on interval data, allowing for partitioning of data points into distinct clusters.

Description

Performs k-means clustering on interval data, allowing for partitioning of data points into distinct clusters.

Usage

ikmeans(
  x,
  centers,
  nstart = 10,
  distance = "euclid",
  trace = FALSE,
  iter.max = 20
)

Arguments

x

A 3D interval array representing the data to be clustered.
centers

Either the number of clusters to create or a set of pre-initialized cluster centers. If a number is provided, it specifies how many clusters to create.
nstart

The number of times to run the k-means algorithm with different starting values in order to find the best solution (default is 10).
distance

A string specifying the distance metric to use: 'euclid' for Euclidean distance or 'hausdorff' for Hausdorff distance (default is 'euclid').
trace

Logical value indicating whether to show progress of the algorithm (default is 'FALSE').
iter.max

Maximum number of iterations allowed for the k-means algorithm (default is 20).

Value

A list of clustering results, including: - 'cluster': A vector indicating the cluster assignment of each data point. - 'centers': The final cluster centers. - 'totss': Total sum of squares. - 'withinss': Within-cluster sum of squares by cluster. - 'tot.withinss': Total within-cluster sum of squares. - 'betweenss': Between-cluster sum of squares. - 'size': The number of points in each cluster. - 'iter': Number of iterations the algorithm executed.

Examples

ikmeans(iaggregate(iris, col = 5), 2)
ikmeans(iaggregate(iris, col = 5), iaggregate(iris, col = 5))

Performs clustering on interval data using the Neo-KM algorithm, which allows for overlapping and non-exhaustive cluster membership.

Description

Performs clustering on interval data using the Neo-KM algorithm, which allows for overlapping and non-exhaustive cluster membership.

Usage

ineokm(
  x,
  centers,
  alpha = 0.3,
  beta = 0.05,
  nstart = 10,
  trace = FALSE,
  iter.max = 20
)

Arguments

x

A 3D interval array representing the data to be clustered.
centers

Either the number of clusters to create or a set of pre-initialized cluster centers. If a number is provided, it specifies how many clusters to create.
alpha

A numeric value that controls the degree of overlap between clusters (default is 0.3).
beta

A numeric value that controls the non-exhaustiveness of clusters (default is 0.05).
nstart

The number of times to run the Neo-KM algorithm with different starting values in order to find the best solution (default is 10).
trace

Logical value indicating whether to show the progress of the algorithm (default is 'FALSE').
iter.max

Maximum number of iterations allowed for the Neo-KM algorithm (default is 20).

Value

A list of clustering results, including: - 'cluster': A vector indicating the cluster assignment of each data point. - 'centers': The final cluster centers. - 'totss': Total sum of squares. - 'withinss': Within-cluster sum of squares by cluster. - 'tot.withinss': Total within-cluster sum of squares. - 'betweenss': Between-cluster sum of squares. - 'size': The number of points in each cluster. - 'iter': Number of iterations the algorithm executed.

Examples

ineokm(iaggregate(iris, col = 5), 3)
ineokm(iaggregate(iris, col = 5), iaggregate(iris, col = 5), 1, 2)

Temperature by month and humidity in european city.

Description

Temperature by month and humidity in european city.

Usage

inter_city

Format

A interval structure with 68 rows and 13 variables divided in min and max with 17 class:

temp.jan
temp.fev
temp.mars
temp.avr
temp.mai
temp.juin
temp.juil
temp.aout
temp.sep
temp.oct
temp.nov
temp.dec
humid

Class :

Allemagne
Angleterre
Autriche
Belgique
Bulgarie
Croatie
Danemark
Espagne
France
Italie
Pays-Bas
Pologne
Portugal
Roumanie
Russie
Turquie
Ukraine

Emotions in music aggregate on BPM to interval multi label data.

Description

Emotions in music aggregate on BPM to interval multi label data.

Usage

inter_emotions

Format

A interval structure with 59 rows and 71 variables divided in min and max with 6 class:

Mean_Acc1298_Mean_Mem40_Centroid
Mean_Acc1298_Mean_Mem40_Rolloff
Mean_Acc1298_Mean_Mem40_Flux
Mean_Acc1298_Mean_Mem40_MFCC_0

...

Mean_Acc1298_Mean_Mem40_MFCC_12
Mean_Acc1298_Std_Mem40_Centroid
Mean_Acc1298_Std_Mem40_Rolloff
Mean_Acc1298_Std_Mem40_Flux
Mean_Acc1298_Std_Mem40_MFCC_0

...

Mean_Acc1298_Std_Mem40_MFCC_12
Std_Acc1298_Mean_Mem40_Centroid
Std_Acc1298_Mean_Mem40_Rolloff
Std_Acc1298_Mean_Mem40_Flux
Std_Acc1298_Mean_Mem40_MFCC_0

...

Std_Acc1298_Mean_Mem40_MFCC_12
Std_Acc1298_Std_Mem40_Centroid
Std_Acc1298_Std_Mem40_Rolloff
Std_Acc1298_Std_Mem40_Flux
Std_Acc1298_Std_Mem40_MFCC_0

...

Std_Acc1298_Std_Mem40_MFCC_12
BH_LowPeakAmp
BH_LowPeakBPM
BH_HighPeakAmp
BH_HighLowRatio
BHSUM1
BHSUM2
BHSUM3

Class :

amazed.suprised
happy.pleased
relaxing.calm
quiet.still
sad.lonely
angry.aggresive

Source

https://mulan.sourceforge.net/datasets-mlc.html

Results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars, aggregate on sulfur dioxide to interval simple label data.

Description

Results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars, aggregate on sulfur dioxide to interval simple label data.

Usage

inter_wine

Format

A interval structure with 132 rows and 10 variables divided in min and max with 7 class:

fixed.acidity
volatile.acidity
citric.acid
residual.sugar
chlorides
total.sulfur.dioxide
density
pH
sulphates
alcohol

Class :

Class3
Class4
Class5
Class6
Class7
Class8
Class9

Source

https://archive.ics.uci.edu/dataset/186/wine+quality

Clusters interval data using the OKM (Overlapping K-means) algorithm.

Description

Clusters interval data using the OKM (Overlapping K-means) algorithm.

Usage

iokm(
  x,
  centers,
  nstart = 10,
  distance = "euclid",
  algorithm = "std",
  update = "mean",
  trace = FALSE,
  iter.max = 20,
  secure = FALSE
)

Arguments

x

A 3D interval array representing the data to be clustered.
centers

Either the number of clusters to create or a set of pre-initialized cluster centers. If a number is provided, it indicates how many clusters to create.
nstart

The number of times to run the OKM algorithm with different starting values to find the best result (default is 10).
distance

A string specifying the distance metric to use: 'euclid' for Euclidean distance or 'hausdorff' for Hausdorff distance (default is 'euclid').
algorithm

A string specifying the algorithm type to use: 'std' for the standard algorithm or 'matrix' for matrix-based algorithm (default is 'std').
update

A string specifying the update method for cluster centers. Either: 'mean', 'sum', 'join' or 'meet' (default is 'mean').
trace

Logical value indicating whether to show progress of the algorithm (default is 'FALSE').
iter.max

Maximum number of iterations allowed for the OKM algorithm (default is 20).
secure

Logical value indicating whether to ensure that the minimum is less than or equal to the maximum in intervals (default is 'FALSE').

Value

A list of clustering results, including: - 'cluster': Matrix indicating the cluster assignment for each data point. - 'centers': The final cluster centers. - 'totss': Total sum of squares. - 'withinss': Within-cluster sum of squares by elements. - 'tot.withinss': Total within-cluster sum of squares. - 'betweenss': Between-cluster sum of squares. - 'size': The number of points in each cluster. - 'iter': The number of iterations the algorithm executed. - 'overlaps': The average overlap across clusters.

Examples

iokm(iaggregate(iris, col = 5), 2)
iokm(iaggregate(iris, col = 5), iaggregate(iris, col = 5))

Determines if an object is a strictly valid interval object.

Description

Determines if an object is a strictly valid interval object.

Usage

is.interval(x)

Arguments

x

An R object to be tested.

Value

A logical value indicating whether the object is a valid interval.

Examples

is.interval(inter_city)
is.interval(1:4)

Plots the overlap of membership degrees in a matrix as a function of a threshold.

Description

Plots the overlap of membership degrees in a matrix as a function of a threshold.

Usage

measure(x, min = 0, max = 1, step = 0.1)

Arguments

x

A matrix of membership degrees.
min

Minimum threshold value for the plot (default is 0).
max

Maximum threshold value for the plot (default is 1).
step

Step size for the threshold values (default is 0.1).

Value

No return value, it plot the overlap as a function of the threshold.

Examples

membership_matrix <- matrix(runif(20), nrow = 5)
measure(membership_matrix, min = 0, max = 1, step = 0.2)

Clusters data using the NEOKM (Non-Exhaustive Overlapping K-means) algorithm.

Description

Clusters data using the NEOKM (Non-Exhaustive Overlapping K-means) algorithm.

Usage

neokm(
  x,
  centers,
  alpha = 0.3,
  beta = 0.05,
  nstart = 10,
  trace = FALSE,
  iter.max = 20
)

Arguments

x

A numeric matrix or data frame containing the data to be clustered.
centers

Either the number of clusters to create or a set of pre-initialized cluster centers. If a number is provided, it indicates how many clusters to create.
alpha

A numeric value representing the degree of overlap allowed between clusters (default is 0.3).
beta

A numeric value representing non-exhaustiveness, which affects the cluster formation (default is 0.05).
nstart

The number of times to run the NEOKM algorithm with different starting values to find the best result (default is 10).
trace

Logical value indicating whether to show progress of the algorithm (default is 'FALSE').
iter.max

Maximum number of iterations allowed for the NEOKM algorithm (default is 20).

Value

A list of clustering results, including: - 'cluster': Matrix indicating the cluster assignment for each data point. - 'centers': The final cluster centers. - 'totss': Total sum of squares. - 'withinss': Within-cluster sum of squares by elements. - 'tot.withinss': Total within-cluster sum of squares. - 'betweenss': Between-cluster sum of squares. - 'size': The number of points in each cluster. - 'iter': The number of iterations the algorithm executed. - 'overlaps': The average overlap across clusters.

Examples

neokm(iris[, -5], 3)
neokm(iris[, -5], iris[, -5], 1, 2)

Clusters data using the OKM (Overlapping K-Means) clustering algorithm.

Description

Clusters data using the OKM (Overlapping K-Means) clustering algorithm.

Usage

okm(x, centers, iter.max = 10, nstart = 1, trace = FALSE, method = "euclid")

Arguments

x

A numeric data matrix or data frame containing the data to be clustered.
centers

Either a positive integer indicating the number of clusters to create or a matrix of pre-initialized cluster centers.
iter.max

Maximum number of iterations allowed for the clustering algorithm (default is 10).
nstart

Number of random initializations to find the best result (default is 1).
trace

Logical value indicating whether to display the progress of the algorithm (default is 'FALSE').
method

A string specifying the distance metric to use; options are 'euclid' (Euclidean distance) or 'manhattan' (Manhattan distance) (default is "euclid").

Value

A list containing the clustering results, including: - 'cluster': Matrix indicating the cluster assignments for each data point. - 'centers': The final cluster centers. - 'tot.withinss': Total within-cluster sum of squares. - 'overlaps': The measure of overlap among clusters.

Examples

okm(iris[, -5], 3)

Generates a visual representation of interval data as rectangles on a plot.

Description

Generates a visual representation of interval data as rectangles on a plot.

Usage

## S3 method for class 'interval'
plot(x, ...)

Arguments

x

An interval object to be plotted.
...

Additional graphical parameters such as 'col' and 'add'.

Value

No return value, it plot the interval.

Examples

plot(iaggregate(iris, 5))
plot(iaggregate(iris, 5), col = 4)
plot(iaggregate(iris, 5), add = TRUE)

Displays the results of fuzzy icmeans clustering in a readable format.

Description

Displays the results of fuzzy icmeans clustering in a readable format.

Usage

## S3 method for class 'icmeans'
print(x, ...)

Arguments

x

An 'icmeans' object resulting from the 'fuzzy_icmeans' function.
...

Additional arguments passed to print().

Value

No return value, it prints the clustering results to the console.

Displays the results of ikmeans clustering in a readable format.

Description

Displays the results of ikmeans clustering in a readable format.

Usage

## S3 method for class 'ikmeans'
print(x, ...)

Arguments

x

An 'ikmeans' object resulting from the 'ikmeans' function.
...

Additional arguments passed to print().

Value

No return value, it prints the clustering results to the console.

Displays the results of Neo-KM clustering in a user-friendly format.

Description

Displays the results of Neo-KM clustering in a user-friendly format.

Usage

## S3 method for class 'ineokm'
print(x, ...)

Arguments

x

An 'ineokm' object resulting from the 'ineokm' function.
...

Additional arguments passed to print().

Value

No return value, it prints the clustering results to the console.

Custom print method for displaying interval objects in a readable format.

Description

Custom print method for displaying interval objects in a readable format.

Usage

## S3 method for class 'interval'
print(x, ...)

Arguments

x

An interval object to be printed.
...

Additional arguments passed to the underlying print() function.

Value

No return value, it prints the interval to the console.

Examples

print(inter_city)

Displays the results of IOKM clustering in a user-friendly format.

Description

Displays the results of IOKM clustering in a user-friendly format.

Usage

## S3 method for class 'iokm'
print(x, ...)

Arguments

x

An 'iokm' object resulting from the 'iokm' function.
...

Additional arguments passed to print().

Value

No return value, it prints the clustering results to the console.

Displays the results of NEOKM clustering in a user-friendly format.

Description

Displays the results of NEOKM clustering in a user-friendly format.

Usage

## S3 method for class 'neokm'
print(x, ...)

Arguments

x

A 'neokm' object resulting from the 'neokm' function.
...

Additional arguments passed to print().

Value

No return value, it prints the clustering results to the console.

Displays the results of OKM clustering in a readable format.

Description

Displays the results of OKM clustering in a readable format.

Usage

## S3 method for class 'okm'
print(x, ...)

Arguments

x

An OKM object resulting from the 'okm' function.
...

Additional arguments passed to print().

Value

No return value, it prints the clustering results to the console.

Displays the results of R1-OKM clustering in a readable format.

Description

Displays the results of R1-OKM clustering in a readable format.

Usage

## S3 method for class 'r1okm'
print(x, ...)

Arguments

x

An R1-OKM object resulting from the 'r1okm' function.
...

Additional arguments passed to print().

Value

No return value, it prints the clustering results to the console.

Displays the results of R2-OKM clustering in a readable format.

Description

Displays the results of R2-OKM clustering in a readable format.

Usage

## S3 method for class 'r2okm'
print(x, ...)

Arguments

x

An R2-OKM object resulting from the 'r2okm' function.
...

Additional arguments passed to print().

Value

No return value, it prints the clustering results to the console.

Cluster data using the R1-OKM algorithm.

Description

Cluster data using the R1-OKM algorithm.

Usage

r1okm(x, centers, alpha = 0, nstart = 10, trace = FALSE, iter.max = 20)

Arguments

x

A numeric data matrix or data frame containing the data to be clustered.
centers

Either a positive integer indicating the number of clusters to create or a matrix of initial cluster centers.
alpha

A numeric parameter controlling the clustering behavior, influencing the degree of overlap between clusters (default is 0).
nstart

Number of random initializations to find the best clustering result (default is 10).
trace

Logical value indicating whether to display progress information during execution (default is 'FALSE').
iter.max

Maximum number of iterations allowed for the clustering algorithm (default is 20).

Value

A list containing the clustering results, including: - 'cluster': Matrix indicating the cluster assignments for each data point. - 'centers': The final cluster centers. - 'totss': Total sum of squares. - 'withinss': Within-cluster sum of squares for each cluster. - 'tot.withinss': Total within-cluster sum of squares. - 'betweenss': Between-cluster sum of squares. - 'size': Number of data points in each cluster. - 'iter': Number of iterations performed. - 'overlaps': Average number of clusters that each point overlaps with.

Examples

r1okm(iris[, -5], 3)
r1okm(iris[, -5], 3, alpha = -0.5)
r1okm(iris[, -5], iris[, -5], alpha = 1)

Cluster data using the R2-OKM algorithm.

Description

Cluster data using the R2-OKM algorithm.

Usage

r2okm(x, centers, lambda = 0, nstart = 10, trace = FALSE, iter.max = 20)

Arguments

x

A numeric data matrix or data frame containing the data to be clustered.
centers

Either a positive integer specifying the number of clusters to create or a matrix of initial cluster centers.
lambda

A numeric parameter that controls the clustering behavior, influencing the shape and separation of clusters (default is 0).
nstart

Number of random initializations to find the best clustering result (default is 10).
trace

Logical value indicating whether to display progress information during execution (default is 'FALSE').
iter.max

Maximum number of iterations allowed for the clustering algorithm (default is 20).

Value

A list containing the clustering results, which includes: - 'cluster': Matrix indicating the cluster assignments for each data point. - 'centers': The final cluster centers. - 'totss': Total sum of squares. - 'withinss': Within-cluster sum of squares for each cluster. - 'tot.withinss': Total within-cluster sum of squares. - 'betweenss': Between-cluster sum of squares. - 'size': Number of data points in each cluster. - 'iter': Number of iterations performed. - 'overlaps': Average number of clusters that each point overlaps with.

Examples

r2okm(iris[, -5], 3)
r2okm(iris[, -5], 3, lambda = 0.3)
r2okm(iris[, -5], iris[, -5], lambda = 1)

Reads a CSV file and converts the data into a 3D interval array.

Description

Reads a CSV file and converts the data into a 3D interval array.

Usage

read.interval(..., row.names = FALSE, class = NULL)

Arguments

...

Additional arguments passed to read.csv().
row.names

Logical indicating if the first column contains row names.
class

The column index of class labels (set to 'NULL' if not present).

Value

A structured interval object representing the data from the CSV file.

Writes an interval object to a CSV file.

Description

Writes an interval object to a CSV file.

Usage

write.interval(x, ..., class = FALSE)

Arguments

x

An interval object to be saved.
...

Additional arguments passed to write.csv().
class

Logical indicating whether to add the class column in the CSV.

Value

No return value, it saves the interval to the given CSV file.