Class represents clustering algorithm DBSCAN. More...

Public Member Functions
def	__init__ (self, data, eps, neighbors, ccore=True, kwargs)
	Constructor of clustering algorithm DBSCAN. More...

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

def	get_clusters (self)
	Returns allocated clusters. More...

def	get_noise (self)
	Returns allocated noise. More...

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

Detailed Description

Class represents clustering algorithm DBSCAN.

This DBSCAN algorithm is KD-tree optimized.

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

Example:

from pyclustering.cluster.dbscan import dbscan
from pyclustering.cluster import cluster_visualizer
from pyclustering.utils import read_sample
from pyclustering.samples.definitions import FCPS_SAMPLES
# Sample for cluster analysis.
sample = read_sample(FCPS_SAMPLES.SAMPLE_CHAINLINK)
# Create DBSCAN algorithm.
dbscan_instance = dbscan(sample, 0.7, 3)
# Start processing by DBSCAN.
dbscan_instance.process()
# Obtain results of clustering.
clusters = dbscan_instance.get_clusters()
noise = dbscan_instance.get_noise()
# Visualize clustering results
visualizer = cluster_visualizer()
visualizer.append_clusters(clusters, sample)
visualizer.append_cluster(noise, sample, marker='x')
visualizer.show()

Definition at line 37 of file dbscan.py.

Constructor & Destructor Documentation

◆ init()

def pyclustering.cluster.dbscan.dbscan.__init__	(	self,
		data,
		eps,
		neighbors,
		ccore = `True`,
		kwargs
	)

Constructor of clustering algorithm DBSCAN.

Parameters

[in]	data	(list): Input data that is presented as list of points (objects), each point should be represented by list or tuple.
[in]	eps	(double): Connectivity radius between points, points may be connected if distance between them less then the radius.
[in]	neighbors	(uint): minimum number of shared neighbors that is required for establish links between points.
[in]	ccore	(bool): if True than DLL CCORE (C++ solution) will be used for solving the problem.
[in]	**kwargs	Arbitrary keyword arguments (available arguments: 'data_type').

Keyword Args:

data_type (string): Data type of input sample 'data' that is processed by the algorithm ('points', 'distance_matrix').

Definition at line 73 of file dbscan.py.

Member Function Documentation

◆ get_cluster_encoding()

def pyclustering.cluster.dbscan.dbscan.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 168 of file dbscan.py.

◆ get_clusters()

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

Returns allocated clusters.

Remarks: Allocated clusters can be returned only after data processing (use method process()). Otherwise empty list is returned.

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

See also: process(); get_noise()

Definition at line 136 of file dbscan.py.

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

◆ get_noise()

def pyclustering.cluster.dbscan.dbscan.get_noise ( self )

Returns allocated noise.

Remarks: Allocated noise can be returned only after data processing (use method process() before). Otherwise empty list is returned.

Returns: (list) List of indexes that are marked as a noise.

See also: process(); get_clusters()

Definition at line 152 of file dbscan.py.

◆ process()

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

Performs cluster analysis in line with rules of DBSCAN algorithm.

See also: get_clusters(); get_noise()

Definition at line 109 of file dbscan.py.

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

pyclustering/cluster/dbscan.py

Public Member Functions