|  | ## DBSCAN

#### Synopsis

Clustering with DBSCAN

#### Description

This operator provides the DBScan cluster algorithm. If no id attribute is present, the operator will create one.

#### Input

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

, expects: ExampleSet

#### Output

- cluster model:
- clustered set:

#### Parameters

- epsilon: Specifies the size of neighbourhood.
- min points: The minimal number of points forming a cluster.
- 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.
- measure types: The measure type
- mixed measure: Select measure
- nominal measure: Select measure
- numerical measure: Select measure
- divergence: Select divergence
- kernel type: The kernel type
- kernel gamma: The kernel parameter gamma.
- kernel sigma1: The kernel parameter sigma1.
- kernel sigma2: The kernel parameter sigma2.
- kernel sigma3: The kernel parameter sigma3.
- kernel degree: The kernel parameter degree.
- kernel shift: The kernel parameter shift.
- kernel a: The kernel parameter a.
- kernel b: The kernel parameter b.

#### ExampleProcess