EM
- Former user (Deleted)
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 |