Synopsis
Clustering with k-medoids
Description
This operator represents an implementation of k-medoids. 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
- 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.