pyclustering.cluster.kmeans.kmeans Class Reference

Class represents K-Means clustering algorithm. More...

Public Member Functions
def	__init__ (self, data, initial_centers, tolerance=0.001, ccore=True, kwargs)
	Constructor of clustering algorithm K-Means. More...
def	process (self)
	Performs cluster analysis in line with rules of K-Means algorithm. More...
def	predict (self, points)
	Calculates the closest cluster to each point. More...
def	get_clusters (self)
	Returns list of allocated clusters, each cluster contains indexes of objects in list of data. More...
def	get_centers (self)
	Returns list of centers of allocated clusters. More...
def	get_total_wce (self)
	Returns sum of metric errors that depends on metric that was used for clustering (by default SSE - Sum of Squared Errors). More...
def	get_cluster_encoding (self)
	Returns clustering result representation type that indicate how clusters are encoded. More...

Detailed Description

Class represents K-Means clustering algorithm.

CCORE implementation of the algorithm uses thread pool to parallelize the clustering process.

K-Means clustering results depend on initial centers. Algorithm K-Means++ can used for initialization initial centers from module 'pyclustering.cluster.center_initializer'.

K-Means clustering results. At the left - 'Simple03.data' sample, at the right - 'Lsun.data' sample.

Example #1 - Clustering using K-Means++ for center initialization:

from pyclustering.cluster.kmeans import kmeans, kmeans_visualizer
from pyclustering.cluster.center_initializer import kmeans_plusplus_initializer
from pyclustering.samples.definitions import FCPS_SAMPLES
from pyclustering.utils import read_sample

# Load list of points for cluster analysis.
sample = read_sample(FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS)

# Prepare initial centers using K-Means++ method.
initial_centers = kmeans_plusplus_initializer(sample, 2).initialize()

# Create instance of K-Means algorithm with prepared centers.
kmeans_instance = kmeans(sample, initial_centers)

# Run cluster analysis and obtain results.
kmeans_instance.process()
clusters = kmeans_instance.get_clusters()
final_centers = kmeans_instance.get_centers()

# Visualize obtained results
kmeans_visualizer.show_clusters(sample, clusters, final_centers)

Example #2 - Clustering using specific distance metric, for example, Manhattan distance:

# prepare input data and initial centers for cluster analysis using K-Means

# create metric that will be used for clustering
manhattan_metric = distance_metric(type_metric.MANHATTAN)

# create instance of K-Means using specific distance metric:
kmeans_instance = kmeans(sample, initial_centers, metric=manhattan_metric)

# run cluster analysis and obtain results
kmeans_instance.process()
clusters = kmeans_instance.get_clusters()

See also

center_initializer

Definition at line 272 of file kmeans.py.

Constructor & Destructor Documentation

__init__()

def pyclustering.cluster.kmeans.kmeans.__init__	(		self,
			data,
			initial_centers,
			tolerance = 0.001,
			ccore = True,
			kwargs
	)

Constructor of clustering algorithm K-Means.

Center initializer can be used for creating initial centers, for example, K-Means++ method.

Parameters

[in]	data	(array_like): Input data that is presented as array of points (objects), each point should be represented by array_like data structure.
[in]	initial_centers	(array_like): Initial coordinates of centers of clusters that are represented by array_like data structure: [center1, center2, ...].
[in]	tolerance	(double): Stop condition: if maximum value of change of centers of clusters is less than tolerance then algorithm stops processing.
[in]	ccore	(bool): Defines should be CCORE library (C++ pyclustering library) used instead of Python code or not.
[in]	**kwargs	Arbitrary keyword arguments (available arguments: 'observer', 'metric', 'itermax').

Keyword Args:

• observer (kmeans_observer): Observer of the algorithm to collect information about clustering process on each iteration.
• metric (distance_metric): Metric that is used for distance calculation between two points (by default euclidean square distance).
• itermax (uint): Maximum number of iterations that is used for clustering process (by default: 200).

See also

center_initializer

Definition at line 326 of file kmeans.py.

Member Function Documentation

get_centers()

def pyclustering.cluster.kmeans.kmeans.get_centers

(

self

)

Returns list of centers of allocated clusters.

See also

process()

get_clusters()

Definition at line 472 of file kmeans.py.

get_cluster_encoding()

def pyclustering.cluster.kmeans.kmeans.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 501 of file kmeans.py.

get_clusters()

def pyclustering.cluster.kmeans.kmeans.get_clusters

(

self

)

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

See also

process()

get_centers()

Definition at line 460 of file kmeans.py.

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

get_total_wce()

def pyclustering.cluster.kmeans.kmeans.get_total_wce

(

self

)

Returns sum of metric errors that depends on metric that was used for clustering (by default SSE - Sum of Squared Errors).

Sum of metric errors is calculated using distance between point and its center:

See also

process()

get_clusters()

Definition at line 487 of file kmeans.py.

predict()

def pyclustering.cluster.kmeans.kmeans.predict	(		self,
			points
	)

Calculates the closest cluster to each point.

Parameters

[in]

points

(array_like): Points for which closest clusters are calculated.

Returns

(list) List of closest clusters for each point. Each cluster is denoted by index. Return empty collection if 'process()' method was not called.

Definition at line 435 of file kmeans.py.

process()

def pyclustering.cluster.kmeans.kmeans.process

(

self

)

Performs cluster analysis in line with rules of K-Means algorithm.

Returns

(kmeans) Returns itself (K-Means instance).

Remarks

Results of clustering can be obtained using corresponding get methods.

See also

get_clusters()

get_centers()

Definition at line 365 of file kmeans.py.

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

• pyclustering/cluster/kmeans.py