| Title: | Subgroup Discovery and Analytics |
|---|---|
| Description: | A collection of efficient and effective tools and algorithms for subgroup discovery and analytics. The package integrates an R interface to the org.vikamine.kernel library of the VIKAMINE system <http://www.vikamine.org> implementing subgroup discovery, pattern mining and analytics in Java. |
| Authors: | Martin Atzmueller |
| Maintainer: | Martin Atzmueller <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.1 |
| Built: | 2024-11-22 06:27:29 UTC |
| Source: | CRAN |
Constructs a target variable, i.e., an object suitable to be passed to DiscoverSubgroups or CreateSDTask.
as.target(attribute, value=NULL)as.target(attribute, value=NULL)
attribute |
The attribute of the target variable. |
value |
For binary targets, the respective attribute value; the value is NULL for numeric targets. |
# creating a target variable # binary: as.target("class", "true") #numeric: as.target("numeric_class")# creating a target variable # binary: as.target("class", "true") #numeric: as.target("numeric_class")
Performs subgroup discovery according to the given task.
CreateSDTask(source, target, config = SDTaskConfig())CreateSDTask(source, target, config = SDTaskConfig())
source |
a data.frame or the a character string giving the filename of an ARFF file to use. Providing a file name directly provides the data to the subgroup discovery algorithms on the Java side, which is more memory efficient than converting the data frame to the Java representation. |
target |
the target variable (constructed by as.target) to consider for subgroup discovery. |
config |
an instance of SDTaskConfig providing various parameters for subgroup discovery. |
DiscoverSubgroups.
DiscoverSubgroupsByTask
SDTaskConfig
# creating a task data(credit.data) # task with binary target task <- CreateSDTask(credit.data, as.target("class", "good")) # task with numeric target taskNum <- CreateSDTask(credit.data, as.target("credit_amount"))# creating a task data(credit.data) # task with binary target task <- CreateSDTask(credit.data, as.target("class", "good")) # task with numeric target taskNum <- CreateSDTask(credit.data, as.target("credit_amount"))
This dataset classifies people described by a set of attributes as good or bad credit risks.
data(credit.data)data(credit.data)
A vector containing 1000 observations.
UCI Repository, https://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data).
Performs subgroup discovery according to the given target and the configuration on the data.
DiscoverSubgroups(source, target, config= SDTaskConfig(), as.df=FALSE)DiscoverSubgroups(source, target, config= SDTaskConfig(), as.df=FALSE)
source |
a data.frame or the a character string giving the filename of an ARFF file to use. Providing a file name directly provides the data to the subgroup discovery algorithms on the Java side, which is more memory efficient than converting the data frame to the Java representation. |
target |
the target variable (constructed by as.target) to consider for subgroup discovery. |
config |
an instance of SDTaskConfig providing various parameters for subgroup discovery. |
as.df |
TRUE, if the result patterns should be returned as
a data.frame using |
DiscoverSubgroupsByTask.
as.target
CreateSDTask
SDTaskConfig
# subgroup discovery on a data.frame, for binary target data(credit.data) result1 <- DiscoverSubgroups( credit.data, as.target("class", "good"), new("SDTaskConfig", attributes=c("checking_status", "credit_amount", "employment", "purpose"))) result2 <- DiscoverSubgroups( credit.data, as.target("class", "good"), new("SDTaskConfig", attributes=c("checking_status", "employment"))) ToDataFrame(result1) ToDataFrame(result2) # subgroup discovery for numeric target variable result3 <- DiscoverSubgroups( credit.data, as.target("credit_amount"), new("SDTaskConfig", attributes=c("checking_status", "employment"))) ToDataFrame(result3)# subgroup discovery on a data.frame, for binary target data(credit.data) result1 <- DiscoverSubgroups( credit.data, as.target("class", "good"), new("SDTaskConfig", attributes=c("checking_status", "credit_amount", "employment", "purpose"))) result2 <- DiscoverSubgroups( credit.data, as.target("class", "good"), new("SDTaskConfig", attributes=c("checking_status", "employment"))) ToDataFrame(result1) ToDataFrame(result2) # subgroup discovery for numeric target variable result3 <- DiscoverSubgroups( credit.data, as.target("credit_amount"), new("SDTaskConfig", attributes=c("checking_status", "employment"))) ToDataFrame(result3)
Performs subgroup discovery according to the given task.
DiscoverSubgroupsByTask(task, as.df=FALSE)DiscoverSubgroupsByTask(task, as.df=FALSE)
task |
a subgroup discovery task constructed by CreateSDTask. |
as.df |
TRUE, if the result patterns should be returned
as a data.frame using |
DiscoverSubgroups.
CreateSDTask
# creating a task data(credit.data) task <- CreateSDTask( credit.data, as.target("class", "bad"), SDTaskConfig( attributes=c("checking_status", "employment"))) taskNum <- CreateSDTask( credit.data, as.target("credit_amount"), SDTaskConfig( attributes=c("checking_status", "employment"))) # running the tasks DiscoverSubgroupsByTask(task) DiscoverSubgroupsByTask(taskNum)# creating a task data(credit.data) task <- CreateSDTask( credit.data, as.target("class", "bad"), SDTaskConfig( attributes=c("checking_status", "employment"))) taskNum <- CreateSDTask( credit.data, as.target("credit_amount"), SDTaskConfig( attributes=c("checking_status", "employment"))) # running the tasks DiscoverSubgroupsByTask(task) DiscoverSubgroupsByTask(taskNum)
Tests whether a pattern and a data list (row of a data frame) match, e.g., for implementing classification methods.
is.pattern.matching(pattern, data.list)is.pattern.matching(pattern, data.list)
pattern |
An instance of class Pattern, e.g., returned by DiscoverSubgroups. |
data.list |
A list having the attributes as 'keys', and the values as respective values of the list. This corresponds, for example, to a row of a data frame. |
A Simple Container holding the results (subgroups, description and parameters) for the Subgroup and Pattern Mining Algorithms
Objects are created by calls of the form
new("Pattern", ...).
description:The subgroup description, as a character vector.
selectors:The subgroup description, given as a list of (simple) selection expressions, where the 'key' is the attribute and the 'value' is the value.
quality:The numeric value denoting the quality of the subgroup pattern as determined by the applied quality function.
size:The size of the subgroup.
parametersAdditional quality parameters of the subgroup.
DiscoverSubgroups.
DiscoverSubgroupsByTask
CreateSDTask
The rsubgroup package contains a set of efficient and effective tools and algorithms for subgroup discovery and analytics. The package integrates an R interface to the org.vikamine.kernel library of the VIKAMINE system (http://www.vikamine.org).
Note: rsubgroup uses rJava. To set the maximum available heap space for Java, the .jinit command of rJava needs to be called before loading rsubgroup, i.e.
library(rJava) .jinit(parameters="-Xmx2048M") # for two gigabytes heap space, for example library(rsubgroup)
Please note that this needs to happen before rJava is used in any way. After the JVM has been initialized (and started), setting the heap space has no effect any more. Therefore, it is recommended to execute the .jinit(...) command right after loading the rJava package.
| Package: | rsubgroup |
| Type: | Package |
| Version: | 0.7 |
| Date: | 2015-07-xx |
| License: | GPL (>= 3) |
| LazyLoad: | yes |
Martin Atzmueller
Maintainer: Martin Atzmueller <[email protected]>
Martin Atzmueller and Frank Puppe. SD-Map - A Fast Algorithm for Exhaustive Subgroup Discovery. Knowledge Discovery in Databases: PKDD 2006, LNAI 4213, pp. 6-17, Springer Verlag, 2006.
Martin Atzmueller and Florian Lemmerich. Fast Subgroup Discovery for Continuous Target Concepts. In: Foundations of Intelligent Systems, LNCS 5722, pp. 35-44, Springer Verlag, 2009.
Florian Lemmerich and Mathias Rohlfs and Martin Atzmueller. Fast Discovery of Relevant Subgroup Patterns. In: Proc. 23rd FLAIRS Conference, AAAI Press, 2010.
Creates a subgroup discovery task configuration, that is, an instance of SDTaskConfig.
A Set of Configuration Settings for the Subgroup and Pattern Mining Algorithms
Objects are created by calls of the form
SDTaskConfig(...).
attributes:The list of attributes to consider for mining. Either a vector of attribute names, or NULL (the default), which includes all attributes.
discretize:Boolean, indicating whether to (automatically)
discretize numeric attributes (default discretize=TRUE. Depends on
parameter nbins. Either creates distinct values, if their number in the
dataset is <= nbins, or applies equal-frequency discretization for the
respective numeric attribute.
method:A mining method; one of
Beam-Search beam,
BSD bsd,
SD-Map sdmap,
SD-Map enabling internal disjunctions sdmap-dis.
The default is method = "sdmap".
nbins:Specifies the number of bins to be used when
discretizing numeric attributes (see discretize above).
qf:A quality function; one of:
Adjusted Residuals ares,
Binomial Test bin,
Chi-Square Test chi2,
Gain gain,
Lift lift,
Piatetsky-Shapiro ps,
Relative Gain relgain,
Weighted Relative Accuracy wracc.
The default is qf = "ps".
k:The maximum number (top-k) of patterns
to discover, i.e., the best k rules according to the selected
quality function. The default is k = 20
minqual:The minimal quality (default minqual = 0).
minsize:The minimal size of a subgroup (as an integer)
(minimal coverage of database records, default minsize = 0).
mintp:The minimal true positive (tp) threshold, an integer
(minimal (absolute) number of true positives in a subgroup, relevant for
binary target concepts only), defaults to mintp = 0
.
maxlen:The maximal length of a description of
a pattern, i.e., the maximal number of conjunctions. This impacts both
understandability and efficiency. Simpler rules are easier to understand,
and a small maxlen will restrict the search space (default maxlen = 7).
nodefaults:Ignore default values, i.e.,
do not include the respective first value (with index 0) of each
attribute (default nodefaults=FALSE, i.e., include all values).
relfilter:Controls, whether irrelevant
patterns are filtered during pattern mining; negatively
impacts performance (default relfilter = FALSE)).
postfilter:Controls, whether a post-processing
filter is applied; one (or a vector) of:
Minimum Improvement (Global) min-improve-global,
checks the patterns against all possible generalizations,
Minimum Improvement (Pattern Set) min-improve-set,
checks the patterns against all their generalizations
in the result set,
Relevancy Filter relevancy, removes patterns that
are strictly irrelevant,
Significant Improvement (Global) sig-improve-global,
removes patterns that do not significantly improve
(default 0.01 level) w.r.t. all their possible generalizations,
Significant Improvement (Set) sig-improve-set,
removes patterns that do not significantly improve
(default 0.01 level) w.r.t. all generalizations in the result set,
Weighted Covering weighted-covering, performs weighted
covering on the data in order to select a covering set of
subgroups while reducing the overlap on the data.
By default no postfilter is set, i.e., postfilter = "".
parfilter:Provides the minimal improvement value for the postfilter (for min-improve-* filters), or the significance level (P) for sig-improve-* filters.
DiscoverSubgroups.
DiscoverSubgroupsByTask
CreateSDTask
Transforms a list/vector of patterns into a data frame for inspection and analysis.
ToDataFrame(patterns, ndigits = 2)ToDataFrame(patterns, ndigits = 2)
patterns |
List/vector of patterns. |
ndigits |
Number of significant digits when printing floats (optional). |