Class represents the clustering algorithm BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies). More...

Public Member Functions
def	__init__ (self, data, number_clusters, branching_factor=50, max_node_entries=200, diameter=0.5, type_measurement=measurement_type.CENTROID_EUCLIDEAN_DISTANCE, entry_size_limit=500, diameter_multiplier=1.5, ccore=True)
	Constructor of clustering algorithm BIRCH. More...

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

def	get_clusters (self)
	Returns list of allocated clusters, each cluster is represented by a list of indexes where each index corresponds to a point in an input dataset. More...

def	get_cf_entries (self)
	Returns CF-entries that encodes an input dataset. More...

def	get_cf_cluster (self)
	Returns list of allocated CF-entry clusters where each cluster is represented by indexes (each index corresponds to CF-entry). More...

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

Detailed Description

Class represents the clustering algorithm BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies).

BIRCH is suitable for large databases. The algorithm incrementally and dynamically clusters incoming multi-dimensional metric data points using the concepts of Clustering Feature and CF tree. A Clustering Feature is a triple summarizing the information that is maintained about a cluster. The Clustering Feature vector is defined as a triple:

$CF=\left ( N, \overrightarrow{LS}, SS \right )$

Example how to extract clusters from 'OldFaithful' sample using BIRCH algorithm:

from pyclustering.cluster.birch import birch
from pyclustering.cluster import cluster_visualizer
from pyclustering.utils import read_sample
from pyclustering.samples.definitions import FAMOUS_SAMPLES
# Sample for cluster analysis (represented by list)
sample = read_sample(FAMOUS_SAMPLES.SAMPLE_OLD_FAITHFUL)
# Create BIRCH algorithm
birch_instance = birch(sample, 2, diameter=3.0)
# Cluster analysis
birch_instance.process()
# Obtain results of clustering
clusters = birch_instance.get_clusters()
# Visualize allocated clusters
visualizer = cluster_visualizer()
visualizer.append_clusters(clusters, sample)
visualizer.show()

Here is the clustering result produced by BIRCH algorithm:

Fig. 1. BIRCH clustering - sample 'OldFaithful'.

Methods 'get_cf_entries' and 'get_cf_clusters' can be used to obtain information how does an input data is encoded. Here is an example how the encoding information can be extracted and visualized:

from pyclustering.cluster.birch import birch
from pyclustering.cluster import cluster_visualizer
from pyclustering.utils import read_sample
from pyclustering.samples.definitions import FCPS_SAMPLES
# Sample 'Lsun' for cluster analysis (represented by list of points)
sample = read_sample(FCPS_SAMPLES.SAMPLE_LSUN)
# Create BIRCH algorithm
birch_instance = birch(sample, 3, diameter=0.5)
# Cluster analysis
birch_instance.process()
# Obtain results of clustering
clusters = birch_instance.get_clusters()
# Obtain information how does the 'Lsun' sample is encoded in the CF-tree.
cf_entries = birch_instance.get_cf_entries()
cf_clusters = birch_instance.get_cf_cluster()
cf_centroids = [entry.get_centroid() for entry in cf_entries]
# Visualize allocated clusters
visualizer = cluster_visualizer(2, 2, titles=["Encoded data by CF-entries", "Data clusters"])
visualizer.append_clusters(cf_clusters, cf_centroids, canvas=0)
visualizer.append_clusters(clusters, sample, canvas=1)
visualizer.show()

Here is the clustering result produced by BIRCH algorithm:

Fig. 2. CF-tree encoding and BIRCH clustering of 'Lsun' sample.

Definition at line 35 of file birch.py.

Constructor & Destructor Documentation

◆ init()

def pyclustering.cluster.birch.birch.__init__	(	self,
		data,
		number_clusters,
		branching_factor = `50`,
		max_node_entries = `200`,
		diameter = `0.5`,
		type_measurement = `measurement_type.CENTROID_EUCLIDEAN_DISTANCE`,
		entry_size_limit = `500`,
		diameter_multiplier = `1.5`,
		ccore = `True`
	)

Constructor of clustering algorithm BIRCH.

Parameters

[in]	data	(list): An input data represented as a list of points (objects) where each point is be represented by list of coordinates.
[in]	number_clusters	(uint): Amount of clusters that should be allocated.
[in]	branching_factor	(uint): Maximum number of successor that might be contained by each non-leaf node in CF-Tree.
[in]	max_node_entries	(uint): Maximum number of entries that might be contained by each leaf node in CF-Tree.
[in]	diameter	(double): CF-entry diameter that used for CF-Tree construction, it might be increase if 'entry_size_limit' is exceeded.
[in]	type_measurement	(measurement_type): Type measurement used for calculation distance metrics.
[in]	entry_size_limit	(uint): Maximum number of entries that can be stored in CF-Tree, if it is exceeded during creation then the 'diameter' is increased and CF-Tree is rebuilt.
[in]	diameter_multiplier	(double): Multiplier that is used for increasing diameter when 'entry_size_limit' is exceeded.
[in]	ccore	(bool): If True than C++ part of the library is used for processing.

Definition at line 116 of file birch.py.

Member Function Documentation

◆ get_cf_cluster()

def pyclustering.cluster.birch.birch.get_cf_cluster ( self )

Returns list of allocated CF-entry clusters where each cluster is represented by indexes (each index corresponds to CF-entry).

Returns: (list) List of allocated CF-entry clusters.

See also: get_cf_entries

Definition at line 207 of file birch.py.

◆ get_cf_entries()

def pyclustering.cluster.birch.birch.get_cf_entries ( self )

Returns CF-entries that encodes an input dataset.

Returns: (list) CF-entries that encodes an input dataset.

See also: get_cf_cluster

Definition at line 195 of file birch.py.

◆ get_cluster_encoding()

def pyclustering.cluster.birch.birch.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 220 of file birch.py.

◆ get_clusters()

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

Returns list of allocated clusters, each cluster is represented by a list of indexes where each index corresponds to a point in an input dataset.

Returns: (list) List of allocated clusters.

See also: process()

Definition at line 181 of file birch.py.

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

◆ process()

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

Performs cluster analysis in line with rules of BIRCH algorithm.

Returns: (birch) Returns itself (BIRCH instance).

See also: get_clusters()

Definition at line 150 of file birch.py.

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

pyclustering/cluster/birch.py

Public Member Functions