|  | ## K-Means (Kernel)

#### Synopsis

Clustering with kernel k-means

#### Description

This operator is an implementation of kernel k means. Kernel K Means uses kernels to estimate distance between objects and clusters. Because of the nature of kernels it is necessary to sum over all elements of a cluster to calculate one distance. So this algorithm is quadratic in number of examples and returns NO CentroidClusterModel, as its older brother KMeans does. 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.
- use weights: Indicates if the weight attribute should be used.
- k: The number of clusters which should be detected.
- 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
- 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