Package

weka.clusterers

Synopsis

Simple EM (expectation maximisation) class.

EM assigns a probability distribution to each instance which indicates the probability of it belonging to each of the clusters. EM can decide how many clusters to create by cross validation, or you may specify apriori how many clusters to generate.

The cross validation performed to determine the number of clusters is done in the following steps:
1. the number of clusters is set to 1
2. the training set is split randomly into 10 folds.
3. EM is performed 10 times using the 10 folds the usual CV way.
4. the loglikelihood is averaged over all 10 results.
5. if loglikelihood has increased the number of clusters is increased by 1 and the program continues at step 2.

The number of folds is fixed to 10, as long as the number of instances in the training set is not smaller 10. If this is the case the number of folds is set equal to the number of instances.

Options

The table below describes the options available for EM.

Option	Description
debug	If set to true, clusterer may output additional info to the console.
displayModelInOldFormat	Use old format for model output. The old format is better when there are many clusters. The new format is better when there are fewer clusters and many attributes.
maxIterations	maximum number of iterations
minStdDev	set minimum allowable standard deviation
numClusters	set number of clusters. -1 to select number of clusters automatically by cross validation.
seed	The random number seed to be used.

Capabilities

The table below describes the capabilites of EM.

Capability	Supported
Class	No class
Attributes	Nominal attributes, Numeric attributes, Missing values, Unary attributes, Empty nominal attributes, Binary attributes
Min # of instances	1