Package 'FeatureImpCluster'

Create random data set with 4 clusters

Description

Create random data set with 4 clusters in a 2 dimensional subspace of a nr_other_vars+2 dimensional space

Usage

create_random_data(n = 10000, nr_other_vars = 4)

Arguments

n	number of points
nr_other_vars	number of other variables / "noise" dimensions

Value

list containing the random data.table and a vector with the true underlying cluster assignments

Examples

create_random_data(n=1e3)

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Feature importance for k-means clustering

Description

This function loops through PermMisClassRate for each variable of the data. The mean misclassification rate over all iterations is interpreted as variable importance.

Usage

FeatureImpCluster(
  clusterObj,
  data,
  basePred = NULL,
  predFUN = NULL,
  sub = 1,
  biter = 10
)

Arguments

clusterObj	a "typical" cluster object. The only requirement is that there must be a prediction function which maps the data to an integer
data	data.table with the same features as the data set used for clustering (or the simply the same data)
basePred	should be equal to results of predFUN(clusterObj,newdata=data); this option saves time when data is a very large data set
predFUN	predFUN(clusterObj,newdata=data) should provide the cluster assignment as a numeric vector; typically this is a wrapper around a build-in prediction function
sub	integer between 0 and 1(=default), indicates that only a subset of the data should be used if <1
biter	the permutation is iterated biter(=5, default) times

Value

A list of

misClassRate

A matrix of the permutation misclassification rate for each variable and each iteration

featureImp

For each row of complete_data, the associated cluster

Examples

set.seed(123)
dat <- create_random_data(n=1e3)$data # random data

library(flexclust)
res <- kcca(dat,k=4)
f <- FeatureImpCluster(res,dat)
plot(f)

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Permutation misclassification rate for single variable

Description

Answers the following question: Using the current partion as a baseline, what is the misclassification rate if a given feature is permuted?

Usage

PermMisClassRate(
  clusterObj,
  data,
  varName,
  basePred = NULL,
  predFUN = NULL,
  sub = 1,
  biter = 5,
  seed = 123
)

Arguments

clusterObj	a "typical" cluster object. The only requirement is that there must be a prediction function which maps the data to an integer
data	data.table with the same features as the data set used for clustering (or the simply the same data)
varName	character; variable name
basePred	should be equal to results of predFUN(clusterObj,newdata=data); this option saves time when data is a very large data set
predFUN	predFUN(clusterObj,newdata=data) should provide the cluster assignment as a numeric vector; typically this is a wrapper around a build-in prediction function
sub	integer between 0 and 1(=default), indicates that only a subset of the data should be used if <1
biter	the permutation is iterated biter(=5, default) times
seed	value for random seed

Value

vector of length biter with the misclassification rate

Examples

set.seed(123)
dat <- create_random_data(n=1e3)$data # random data

library(flexclust)
res <- kcca(dat,k=4)
PermMisClassRate(res,dat,varName="x")

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Feature importance box plot

Description

Feature importance box plot

Usage

## S3 method for class 'featImpCluster'
plot(x, dat = NULL, color = "none", showPoints = FALSE, ...)

Arguments

x	an object returned from FeatureImpCluster
dat	same data as used for the computation of the feature importance (only relevant for colored plots)
color	If set to "type", the plot will show different variable types with a different color.
showPoints	Show points (default is False)
...	arguments to be passed to base plot method

Value

Returns a ggplot2 object

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