|  | ## Support Vector Clustering

#### Synopsis

Clustering with support vectors

#### Description

An implementation of Support Vector Clustering based on {@rapidminer.cite BenHur/etal/2001a}. This operator will create a cluster attribute if not present yet.

#### Input

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

#### 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.
- min pts: The minimal number of points in each cluster.
- kernel type: The SVM kernel type
- kernel gamma: The SVM kernel parameter gamma (radial).
- kernel degree: The SVM kernel parameter degree (polynomial).
- kernel a: The SVM kernel parameter a (neural).
- kernel b: The SVM kernel parameter b (neural).
- kernel cache: Size of the cache for kernel evaluations im MB
- convergence epsilon: Precision on the KKT conditions
- max iterations: Stop after this many iterations
- p: The fraction of allowed outliers.
- r: Use this radius instead of the calculated one (-1 for calculated radius).
- number sample points: The number of virtual sample points to check for neighborship.

#### ExampleProcess