|  | ## K-Means

#### Synopsis

Clustering with k-means

#### Description

This operator represents an implementation of k-means. This operator will create a cluster attribute if not present yet.

#### Input

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

, expects: ExampleSet

#### Output

- cluster model:
- clustered set:

#### Parameters

- 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.
- k: The number of clusters which should be detected.
- max runs: The maximal number of runs of k-Means with random initialization that are performed.
- max optimization steps: The maximal number of iterations performed for one run of k-Means.
- use local random seed: Indicates if a local random seed should be used.
- local random seed: Specifies the local random seed

#### ExampleProcess