|  | ## Expectation Maximization Clustering

#### Synopsis

Clustering with EM

#### Description

This operator represents an implementation of the EM-algorithm.

#### Input

- example set: expects: ExampleSetMetaData: #examples: = 0; #attributes: 0

#### Output

- cluster model:
- clustered set:

#### Parameters

- k: The number of clusters which should be found.
- add cluster attribute: Indicates if a cluster id is generated as new special attribute.
- add as label: Should the cluster values be added as label.
- remove unlabeled: Delete the unlabeled examples.
- max runs: The maximal number of runs of this operator with random initialization that are performed.
- max optimization steps: The maximal number of iterations performed for one run of this operator.
- quality: The quality that must be fullfilled before the algorithm stops. (The rising of the loglikelyhood that must be undercut)
- use local random seed: Indicates if a local random seed should be used.
- local random seed: Specifies the local random seed
- show probabilities: Insert probabilities for every cluster with every example in the example set.
- inital distribution: Indicates the inital distribution of the centroids.
- correlated attributes: Has to be activated, if the example set contains correlated attributes.

#### ExampleProcess