The class represents clustering algorithm ROCK. More...

Public Member Functions
def	__init__ (self, data, eps, number_clusters, threshold=0.5, ccore=True)
	Constructor of clustering algorithm ROCK. More...

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

The class represents clustering algorithm ROCK.

Example:

from pyclustering.cluster import cluster_visualizer
from pyclustering.cluster.rock import rock
from pyclustering.samples.definitions import FCPS_SAMPLES
from pyclustering.utils import read_sample
 
# Read sample for clustering from file.
sample = read_sample(FCPS_SAMPLES.SAMPLE_HEPTA)
 
# Create instance of ROCK algorithm for cluster analysis. Seven clusters should be allocated.
rock_instance = rock(sample, 1.0, 7)
 
# Run cluster analysis.
rock_instance.process()
 
# Obtain results of clustering.
clusters = rock_instance.get_clusters()
 
# Visualize clustering results.
visualizer = cluster_visualizer()
visualizer.append_clusters(clusters, sample)
visualizer.show()

Definition at line 22 of file rock.py.

Constructor & Destructor Documentation

◆ init()

def pyclustering.cluster.rock.rock.__init__	(	self,
		data,
		eps,
		number_clusters,
		threshold = `0.5`,
		ccore = `True`
	)

Constructor of clustering algorithm ROCK.

Parameters

[in]	data	(list): Input data - list of points where each point is represented by list of coordinates.
[in]	eps	(double): Connectivity radius (similarity threshold), points are neighbors if distance between them is less than connectivity radius.
[in]	number_clusters	(uint): Defines number of clusters that should be allocated from the input data set.
[in]	threshold	(double): Value that defines degree of normalization that influences on choice of clusters for merging during processing.
[in]	ccore	(bool): Defines should be CCORE (C++ pyclustering library) used instead of Python code or not.

Definition at line 53 of file rock.py.

Member Function Documentation

◆ get_cluster_encoding()

def pyclustering.cluster.rock.rock.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 127 of file rock.py.

◆ get_clusters()

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

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

Returns: (list) List of allocated clusters, each cluster contains indexes of objects in list of data.

See also: process()

Definition at line 114 of file rock.py.

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

◆ process()

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

Performs cluster analysis in line with rules of ROCK algorithm.

Returns: (rock) Returns itself (ROCK instance).

See also: get_clusters()

Definition at line 84 of file rock.py.

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

pyclustering/cluster/rock.py

Public Member Functions