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, kwargs)
	Constructor of clustering algorithm X-Means. More...

def	process (self)
	Performs cluster analysis in line with rules of X-Means algorithm. 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_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...

def	get_total_wce (self)
	Returns sum of Euclidean Squared metric errors (SSE - Sum of Squared Errors). 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 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()
# Print total sum of metric errors
print("Total WCE:", xmeans_instance.get_total_wce())
# Visualize clustering results
visualizer = cluster_visualizer()
visualizer.append_clusters(clusters, sample)
visualizer.append_cluster(centers, None, marker='*', markersize=10)
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`,
		kwargs
	)

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.
[in]	**kwargs	Arbitrary keyword arguments (available arguments: 'repeat').

Keyword Args:

repeat (unit): How many times K-Means should be run to improve parameters (by default is 1). With larger 'repeat' values suggesting higher probability of finding global optimum.

Definition at line 129 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(); get_total_wce()

Definition at line 284 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 299 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(); get_total_wce()

Definition at line 269 of file xmeans.py.

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

◆ get_total_wce()

def pyclustering.cluster.xmeans.xmeans.get_total_wce ( self )

Returns sum of Euclidean Squared metric errors (SSE - Sum of Squared Errors).

Sum of metric errors is calculated using distance between point and its center:

$error=\sum_{i=0}^{N}euclidean_square_distance(x_{i}-center(x_{i}))$

See also: process(); get_clusters()

Definition at line 312 of file xmeans.py.

◆ predict()

def pyclustering.cluster.xmeans.xmeans.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.

An example how to calculate (or predict) the closest cluster to specified points.

from pyclustering.cluster.xmeans import xmeans
from pyclustering.samples.definitions import SIMPLE_SAMPLES
from pyclustering.utils import read_sample
# Load list of points for cluster analysis.
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE3)
# Initial centers for sample 'Simple3'.
initial_centers = [[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]]
# Create instance of X-Means algorithm with prepared centers.
xmeans_instance = xmeans(sample, initial_centers)
# Run cluster analysis.
xmeans_instance.process()
# Calculate the closest cluster to following two points.
points = [[0.25, 0.2], [2.5, 4.0]]
closest_clusters = xmeans_instance.predict(points)
print(closest_clusters)

Definition at line 222 of file xmeans.py.

◆ process()

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

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

Returns: (xmeans) Returns itself (X-Means instance).

See also: get_clusters(); get_centers()

Definition at line 169 of file xmeans.py.

Referenced by pyclustering.cluster.xmeans.xmeans.get_total_wce().

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

pyclustering/cluster/xmeans.py

Public Member Functions