EM (3.7)

Learner

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.

(based on WEKA 3.7)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify classifier-specific parameters.

Input Ports

  1. Type: Data Training data

Output Ports

  1. Type: Weka 3.7 Cluster Trained model

Find here

Analytics > Mining > Weka > Weka (3.7) > Cluster Algorithms

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