pyclustering.cluster.silhouette.silhouette_ksearch_type Class Reference

Defines algorithms that can be used to find optimal number of cluster using Silhouette method. More...

+ Inheritance diagram for pyclustering.cluster.silhouette.silhouette_ksearch_type:
+ Collaboration diagram for pyclustering.cluster.silhouette.silhouette_ksearch_type:

Public Member Functions

def get_type (self)
 Returns algorithm type that corresponds to specified enumeration value. More...
 

Static Public Attributes

int KMEANS = 0
 K-Means algorithm for searching optimal number of clusters. More...
 
int KMEDIANS = 1
 K-Medians algorithm for searching optimal number of clusters. More...
 
int KMEDOIDS = 2
 K-Medoids algorithm for searching optimal number of clusters. More...
 

Detailed Description

Defines algorithms that can be used to find optimal number of cluster using Silhouette method.

See also
silhouette_ksearch

Definition at line 234 of file silhouette.py.

Member Function Documentation

◆ get_type()

def pyclustering.cluster.silhouette.silhouette_ksearch_type.get_type (   self)

Returns algorithm type that corresponds to specified enumeration value.

Returns
(type) Algorithm type for cluster analysis.

Definition at line 251 of file silhouette.py.

Member Data Documentation

◆ KMEANS

int pyclustering.cluster.silhouette.silhouette_ksearch_type.KMEANS = 0
static

K-Means algorithm for searching optimal number of clusters.

Definition at line 243 of file silhouette.py.

◆ KMEDIANS

int pyclustering.cluster.silhouette.silhouette_ksearch_type.KMEDIANS = 1
static

K-Medians algorithm for searching optimal number of clusters.

Definition at line 246 of file silhouette.py.

◆ KMEDOIDS

int pyclustering.cluster.silhouette.silhouette_ksearch_type.KMEDOIDS = 2
static

K-Medoids algorithm for searching optimal number of clusters.

Definition at line 249 of file silhouette.py.


The documentation for this class was generated from the following file: