Class represents clustering algorithm CURE with KD-tree optimization. More...

Public Member Functions
def	__init__ (self, data, number_cluster, number_represent_points=5, compression=0.5, ccore=True)
	Constructor of clustering algorithm CURE. More...

def	process (self)
	Performs cluster analysis in line with rules of CURE algorithm. More...

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

def	get_representors (self)
	Returns list of point-representors of each cluster. More...

def	get_means (self)
	Returns list of mean values of each cluster. More...

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

Detailed Description

Class represents clustering algorithm CURE with KD-tree optimization.

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

Example:

# read data for clustering from some file
sample = read_sample(path_to_data);
# create instance of cure algorithm for cluster analysis
# request for allocation of two clusters.
cure_instance = cure(sample, 2, 5, 0.5, True);
# run cluster analysis
cure_instance.process();
# get results of clustering
clusters = cure_instance.get_clusters();

Definition at line 89 of file cure.py.

Constructor & Destructor Documentation

◆ init()

def pyclustering.cluster.cure.cure.__init__	(	self,
		data,
		number_cluster,
		number_represent_points = `5`,
		compression = `0.5`,
		ccore = `True`
	)

Constructor of clustering algorithm CURE.

Parameters

[in]	data	(array_like): Input data that should be processed.
[in]	number_cluster	(uint): Number of clusters that should be allocated.
[in]	number_represent_points	(uint): Number of representative points for each cluster.
[in]	compression	(double): Coefficient defines level of shrinking of representation points toward the mean of the new created cluster after merging on each step. Usually it destributed from 0 to 1.
[in]	ccore	(bool): If True then CCORE (C++ solution) will be used for solving.

Definition at line 112 of file cure.py.

Member Function Documentation

◆ get_cluster_encoding()

def pyclustering.cluster.cure.cure.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 293 of file cure.py.

◆ get_clusters()

def pyclustering.cluster.cure.cure.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_representors(); get_means()

Definition at line 248 of file cure.py.

Referenced by pyclustering.samples.answer_reader.get_cluster_lengths(), and pyclustering.cluster.optics.optics.process().

◆ get_means()

def pyclustering.cluster.cure.cure.get_means ( self )

Returns list of mean values of each cluster.

Cluster index should be used for navigation between mean values.

Returns: (list) List of mean values of each cluster.

See also: get_clusters(); get_representors()

Definition at line 278 of file cure.py.

◆ get_representors()

def pyclustering.cluster.cure.cure.get_representors ( self )

Returns list of point-representors of each cluster.

Cluster index should be used for navigation between lists of point-representors.

Returns: (list) List of point-representors of each cluster.

See also: get_clusters(); get_means()

Definition at line 263 of file cure.py.

◆ process()

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

Performs cluster analysis in line with rules of CURE algorithm.

Remarks: Results of clustering can be obtained using corresponding get methods.

See also: get_clusters()

Definition at line 141 of file cure.py.

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

pyclustering/cluster/cure.py

Public Member Functions