pyclustering.cluster.xmeans.xmeans Class Reference

Class represents clustering algorithm X-Means. More...

Public Member Functions
def	__init__ (self, data, initial_centers=None, kmax=20, tolerance=0.025, criterion=splitting_type.BAYESIAN_INFORMATION_CRITERION, ccore=True)
	Constructor of clustering algorithm X-Means. More...
def	process (self)
	Performs cluster analysis in line with rules of X-Means algorithm. More...
def	get_clusters (self)
	Returns list of allocated clusters, each cluster contains indexes of objects in list of data. More...
def	get_centers (self)
	Returns list of centers for allocated clusters. More...
def	get_cluster_encoding (self)
	Returns clustering result representation type that indicate how clusters are encoded. More...

Detailed Description

Class represents clustering algorithm X-Means.

X-means clustering method starts with the assumption of having a minimum number of clusters, and then dynamically increases them. X-means uses specified splitting criterion to control the process of splitting clusters. Method K-Means++ can be used for calculation of initial centers.

CCORE option can be used to use the pyclustering core - C/C++ shared library for processing that significantly increases performance.

CCORE implementation of the algorithm uses thread pool to parallelize the clustering process.

Here example how to perform cluster analysis using X-Means algorithm:

from pyclustering.cluster import cluster_visualizer
from pyclustering.cluster.xmeans import xmeans
from pyclustering.cluster.center_initializer import kmeans_plusplus_initializer
from pyclustering.utils import read_sample
from pyclustering.samples.definitions import SIMPLE_SAMPLES

# Read sample 'simple3' from file.
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE3)

# Prepare initial centers - amount of initial centers defines amount of clusters from which X-Means will
# start analysis.
amount_initial_centers = 2
initial_centers = kmeans_plusplus_initializer(sample, amount_initial_centers).initialize()

# Create instance of X-Means algorithm. The algorithm will start analysis from 2 clusters, the maximum
# number of clusters that can be allocated is 20.
xmeans_instance = xmeans(sample, initial_centers, 20)
xmeans_instance.process()

# Extract clustering results: clusters and their centers
clusters = xmeans_instance.get_clusters()
centers = xmeans_instance.get_centers()

# Visualize clustering results
visualizer = cluster_visualizer()
visualizer.append_clusters(clusters, sample)
visualizer.append_cluster(centers, None, marker='*')
visualizer.show()

Visualization of clustering results that were obtained using code above and where X-Means algorithm allocates four clusters.

Fig. 1. X-Means clustering results (data 'Simple3').

See also

center_initializer

Definition at line 78 of file xmeans.py.

Constructor & Destructor Documentation

__init__()

def pyclustering.cluster.xmeans.xmeans.__init__	(		self,
			data,
			initial_centers = None,
			kmax = 20,
			tolerance = 0.025,
			criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION,
			ccore = True
	)

Constructor of clustering algorithm X-Means.

Parameters

[in]	data	(list): Input data that is presented as list of points (objects), each point should be represented by list or tuple.
[in]	initial_centers	(list): Initial coordinates of centers of clusters that are represented by list: [center1, center2, ...], if it is not specified then X-Means starts from the random center.
[in]	kmax	(uint): Maximum number of clusters that can be allocated.
[in]	tolerance	(double): Stop condition for each iteration: if maximum value of change of centers of clusters is less than tolerance than algorithm will stop processing.
[in]	criterion	(splitting_type): Type of splitting creation.
[in]	ccore	(bool): Defines should be CCORE (C++ pyclustering library) used instead of Python code or not.

Definition at line 128 of file xmeans.py.

Member Function Documentation

get_centers()

def pyclustering.cluster.xmeans.xmeans.get_centers

(

self

)

Returns list of centers for allocated clusters.

Returns

(list) List of centers for allocated clusters.

See also

process()

get_clusters()

Definition at line 204 of file xmeans.py.

get_cluster_encoding()

def pyclustering.cluster.xmeans.xmeans.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 218 of file xmeans.py.

get_clusters()

def pyclustering.cluster.xmeans.xmeans.get_clusters

(

self

)

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

Returns

(list) List of allocated clusters.

See also

process()

get_centers()

Definition at line 190 of file xmeans.py.

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

process()

def pyclustering.cluster.xmeans.xmeans.process

(

self

)

Performs cluster analysis in line with rules of X-Means algorithm.

Remarks

Results of clustering can be obtained using corresponding gets methods.

See also

get_clusters()

get_centers()

Definition at line 159 of file xmeans.py.

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

• pyclustering/cluster/xmeans.py