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... | |
Class represents clustering algorithm BIRCH.
Example how to extract clusters from 'OldFaithful' sample using BIRCH algorithm:
def pyclustering.cluster.birch.birch.__init__ | ( | self, | |
data, | |||
number_clusters, | |||
branching_factor = 5 , |
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max_node_entries = 5 , |
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initial_diameter = 0.1 , |
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type_measurement = measurement_type.CENTROID_EUCLIDEAN_DISTANCE , |
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entry_size_limit = 200 , |
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diameter_multiplier = 1.5 , |
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ccore = True |
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) |
Constructor of clustering algorithm BIRCH.
[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. |
def pyclustering.cluster.birch.birch.get_cluster_encoding | ( | self | ) |
Returns clustering result representation type that indicate how clusters are encoded.
def pyclustering.cluster.birch.birch.get_clusters | ( | self | ) |
Returns list of allocated clusters, each cluster contains indexes of objects in list of data.
Definition at line 127 of file birch.py.
Referenced by pyclustering.samples.answer_reader.get_cluster_lengths(), and pyclustering.cluster.optics.optics.process().
def pyclustering.cluster.birch.birch.process | ( | self | ) |
Performs cluster analysis in line with rules of BIRCH algorithm.