pyclustering.cluster.somsc.somsc Class Reference

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

Public Member Functions

def __init__ (self, data, amount_clusters, epouch=100, ccore=True)
 Creates SOM-SC (Self Organized Map for Simple Clustering) algorithm for clustering analysis. More...
 
def process (self)
 Performs cluster analysis by competition between neurons of SOM. 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 simple clustering algorithm based on 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 clusters. Algorithm is able 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 37 of file somsc.py.

Constructor & Destructor Documentation

◆ __init__()

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

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.

Definition at line 68 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 115 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 104 of file somsc.py.

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

◆ process()

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

Performs cluster analysis by competition between neurons of SOM.

Returns
(somsc) Returns itself (SOM Simple Clustering instance).
See also
get_clusters()

Definition at line 89 of file somsc.py.


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