Class represents a simple clustering algorithm based on the self-organized feature map. More...

Public Member Functions
def	__init__ (self, data, amount_clusters, epouch=100, ccore=True, **kwargs)
	Creates SOM-SC (Self Organized Map for Simple Clustering) algorithm for clustering analysis. More...

def	process (self)
	Performs cluster analysis by competition between neurons in self-organized map. More...

def	predict (self, points)
	Calculates the closest cluster to each point. More...

def	get_clusters (self)
	Returns list of allocated clusters, each cluster contains indexes of objects in list of data. More...

def	get_cluster_encoding (self)
	Returns clustering result representation type that indicate how clusters are encoded. More...

Detailed Description

Class represents a simple clustering algorithm based on the self-organized feature map.

This algorithm uses amount of clusters that should be allocated as a size of SOM map. Captured objects by neurons are considered as clusters. The algorithm is designed to process data with Gaussian distribution that has spherical forms.

Example:

from pyclustering.cluster import cluster_visualizer
from pyclustering.cluster.somsc import somsc
from pyclustering.samples.definitions import FCPS_SAMPLES
from pyclustering.utils import read_sample
 
# Load list of points for cluster analysis
sample = read_sample(FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS)
 
# Create instance of SOM-SC algorithm to allocated two clusters
somsc_instance = somsc(sample, 2)
 
# Run cluster analysis and obtain results
somsc_instance.process()
clusters = somsc_instance.get_clusters()
 
# Visualize clustering results.
visualizer = cluster_visualizer()
visualizer.append_clusters(clusters, sample)
visualizer.show()

Definition at line 22 of file somsc.py.

Constructor & Destructor Documentation

◆ init()

def pyclustering.cluster.somsc.somsc.__init__	(		self,
			data,
			amount_clusters,
			epouch = `100`,
			ccore = `True`,
		**	kwargs
	)

Creates SOM-SC (Self Organized Map for Simple Clustering) algorithm for clustering analysis.

Parameters

[in]	data	(list): List of points that are used for processing.
[in]	amount_clusters	(uint): Amount of clusters that should be allocated.
[in]	epouch	(uint): Number of epochs for training of SOM.
[in]	ccore	(bool): If it is True then CCORE implementation will be used for clustering analysis.
[in]	**kwargs	Arbitrary keyword arguments (available arguments: `random_state`).

Keyword Args:

random_state (int): Seed for random state (by default is None, current system time is used).

Definition at line 54 of file somsc.py.

Member Function Documentation

◆ get_cluster_encoding()

def pyclustering.cluster.somsc.somsc.get_cluster_encoding ( self )

Returns clustering result representation type that indicate how clusters are encoded.

Returns: (type_encoding) Clustering result representation.

See also: get_clusters()

Definition at line 132 of file somsc.py.

◆ get_clusters()

def pyclustering.cluster.somsc.somsc.get_clusters ( self )

Returns list of allocated clusters, each cluster contains indexes of objects in list of data.

See also: process()

Definition at line 121 of file somsc.py.

Referenced by pyclustering.samples.answer_reader.get_cluster_lengths().

◆ predict()

def pyclustering.cluster.somsc.somsc.predict	(	self,
		points
	)

Calculates the closest cluster to each point.

Parameters

[in] points (array_like): Points for which closest clusters are calculated.

Returns: (list) List of closest clusters for each point. Each cluster is denoted by index. Return empty collection if 'process()' method was not called.

Definition at line 102 of file somsc.py.

◆ process()

def pyclustering.cluster.somsc.somsc.process ( self )

Performs cluster analysis by competition between neurons in self-organized map.

Returns: (somsc) Returns itself (SOM Simple Clustering instance).

See also: get_clusters()

Definition at line 83 of file somsc.py.

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

pyclustering/cluster/somsc.py

Public Member Functions