Class represents clustering algorithm BIRCH. More...

Public Member Functions
def	__init__ (self, data, number_clusters, branching_factor=5, max_node_entries=5, initial_diameter=0.1, type_measurement=measurement_type.CENTROID_EUCLIDEAN_DISTANCE, entry_size_limit=200, 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 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

Class represents clustering algorithm BIRCH.

Example:

# sample for cluster analysis (represented by list)
sample = read_sample(path_to_sample);
# create object of birch that uses CCORE for processing
birch_instance = birch(sample, 2, 5, 5, 0.05, measurement_type.CENTROID_EUCLIDIAN_DISTANCE, 200, True);
# cluster analysis
birch_instance.process();
# obtain results of clustering
clusters = birch_instance.get_clusters();

Definition at line 35 of file birch.py.

Constructor & Destructor Documentation

◆ init()

def pyclustering.cluster.birch.birch.__init__	(	self,
		data,
		number_clusters,
		branching_factor = `5`,
		max_node_entries = `5`,
		initial_diameter = `0.1`,
		type_measurement = `measurement_type.CENTROID_EUCLIDEAN_DISTANCE`,
		entry_size_limit = `200`,
		diameter_multiplier = `1.5`,
		ccore = `True`
	)

Constructor of clustering algorithm BIRCH.

Parameters

[in]	data	(list): Input data presented as list of points (objects), where each point should be represented by list or tuple.
[in]	number_clusters	(uint): Number 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]	initial_diameter	(double): Initial diameter that used for CF-Tree construction, it can 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 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 DLL CCORE (C++ solution) will be used for solving the problem.

Remarks: Despite eight arguments only the first two is mandatory, others can be ommitted. In this case default values are used for instance creation.

Example:

birch_instance1 = birch(sample1, 2);    # two clusters should be allocated
birch_instance2 = birch(sample2, 5);    # five clusters should be allocated
# three clusters should be allocated, but also each leaf node can have maximum 5 
# entries and each entry can have maximum 5 descriptors with initial diameter 0.05.
birch_instance3 = birch(sample3, 3, 5, 5, 0.05);

Definition at line 56 of file birch.py.

Member Function Documentation

◆ 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 143 of file birch.py.

◆ get_clusters()

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

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

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

Returns: (list) List of allocated clusters.

See also: process(); get_noise()

Definition at line 127 of file birch.py.

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

◆ process()

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

Performs cluster analysis in line with rules of BIRCH algorithm.

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

See also: get_clusters()

Definition at line 99 of file birch.py.

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

pyclustering/cluster/birch.py

Public Member Functions