This inheritance list is sorted roughly, but not completely, alphabetically:

[detail level 1234]

Cpyclustering.cluster.agglomerative.agglomerative	Class represents agglomerative algorithm for cluster analysis
Cpyclustering.samples.answer_reader	Answer reader for samples that are used by pyclustering library
Cpyclustering.cluster.bang.bang	Class implements BANG grid based clustering algorithm
Cpyclustering.cluster.bang.bang_animator	Provides service for creating 2-D animation using BANG clustering results
Cpyclustering.cluster.bang.bang_block	BANG-block that represent spatial region in data space
Cpyclustering.cluster.bang.bang_directory	BANG directory stores BANG-blocks that represents grid in data space
Cpyclustering.cluster.bang.bang_visualizer	Visualizer of BANG algorithm's results
Cpyclustering.cluster.birch.birch	Class represents the clustering algorithm BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies)
►Cpyclustering.cluster.bsas.bsas	Class represents BSAS clustering algorithm - basic sequential algorithmic scheme
Cpyclustering.cluster.mbsas.mbsas	Class represents MBSAS (Modified Basic Sequential Algorithmic Scheme)
Cpyclustering.cluster.ttsas.ttsas	Class represents TTSAS (Two-Threshold Sequential Algorithmic Scheme)
Cpyclustering.cluster.bsas.bsas_visualizer	Visualizer of BSAS algorithm's results
Cpyclustering.cluster.canvas_cluster_descr	Description of cluster for representation on canvas
Cpyclustering.nnet.dynamic_visualizer.canvas_descr	Describes plot where dynamic is displayed
Cpyclustering.nnet.hhn.central_element	Central element consist of two central neurons that are described by a little bit different dynamic than peripheral
Cpyclustering.container.cftree.cfentry	Clustering feature representation
►Cpyclustering.container.cftree.cfnode	Representation of node of CF-Tree
Cpyclustering.container.cftree.leaf_node	Represents clustering feature leaf node
Cpyclustering.container.cftree.non_leaf_node	Representation of clustering feature non-leaf node
Cpyclustering.container.cftree.cftree	CF-Tree representation
Cpyclustering.cluster.clarans.clarans	Class represents clustering algorithm CLARANS (a method for clustering objects for spatial data mining)
Cpyclustering.cluster.clique.clique	Class implements CLIQUE grid based clustering algorithm
Cpyclustering.cluster.clique.clique_block	CLIQUE block contains information about its logical location in grid, spatial location in data space and points that are covered by the block
Cpyclustering.cluster.clique.clique_visualizer	Visualizer of CLIQUE algorithm's results
Cpyclustering.cluster.encoder.cluster_encoder	Provides service to change clustering result representation
Cpyclustering.cluster.cluster_visualizer	Common visualizer of clusters on 1D, 2D or 3D surface
Cpyclustering.cluster.cluster_visualizer_multidim	Visualizer for cluster in multi-dimensional data
Cpyclustering.nnet.cnn.cnn_dynamic	Container of output dynamic of the chaotic neural network where states of each neuron during simulation are stored
Cpyclustering.nnet.cnn.cnn_network	Chaotic neural network based on system of logistic map where clustering phenomenon can be observed
Cpyclustering.nnet.cnn.cnn_visualizer	Visualizer of output dynamic of chaotic neural network (CNN)
Cpyclustering.utils.color.color	Consists titles of colors that are used by pyclustering for visualization
Cpyclustering.cluster.clique.coordinate_iterator	Coordinate iterator is used to generate logical location description for each CLIQUE block
Cpyclustering.cluster.cure.cure	Class represents clustering algorithm CURE with KD-tree optimization
Cpyclustering.cluster.cure.cure_cluster	Represents data cluster in CURE term
Cpyclustering.cluster.generator.data_generator	Data generator provides services to generate data with clusters with normal distribution
Cpyclustering.cluster.dbscan.dbscan	Class represents clustering algorithm DBSCAN
Cpyclustering.utils.metric.distance_metric	Distance metric performs distance calculation between two points in line with encapsulated function, for example, euclidean distance or chebyshev distance, or even user-defined
Cpyclustering.gcolor.dsatur.dsatur	Represents DSATUR algorithm for graph coloring problem that uses greedy strategy
Cpyclustering.nnet.dynamic_visualizer.dynamic_descr	Output dynamic description that used to display
Cpyclustering.nnet.dynamic_visualizer.dynamic_visualizer	Basic output dynamic visualizer
Cpyclustering.cluster.elbow.elbow	Class represents Elbow method that is used to find out appropriate amount of clusters in a dataset
Cpyclustering.cluster.ema.ema	Expectation-Maximization clustering algorithm for Gaussian Mixture Model (GMM)
Cpyclustering.cluster.ema.ema_initializer	Provides servies for preparing initial means and covariances for Expectation-Maximization algorithm
Cpyclustering.cluster.ema.ema_observer	Observer of EM algorithm for collecting algorithm state on each step
Cpyclustering.cluster.ema.ema_visualizer	Visualizer of EM algorithm's results
Cpyclustering.cluster.fcm.fcm	Class represents Fuzzy C-means (FCM) clustering algorithm
Cpyclustering.nnet.fsync.fsync_dynamic	Represents output dynamic of Sync in frequency domain
Cpyclustering.nnet.fsync.fsync_visualizer	Visualizer of output dynamic of sync network in frequency domain
Cpyclustering.cluster.ga.ga_observer	Genetic algorithm observer that is used to collect information about clustering process on each iteration
Cpyclustering.cluster.ga.ga_visualizer	Genetic algorithm visualizer is used to show clustering results that are specific for this particular algorithm: clusters, evolution of global and local optimum
Cpyclustering.cluster.ga.genetic_algorithm	Class represents Genetic clustering algorithm
Cpyclustering.cluster.gmeans.gmeans	Class implements G-Means clustering algorithm
Cpyclustering.utils.graph.graph	Graph representation
Cpyclustering.nnet.hhn.hhn_parameters	Describes parameters of Hodgkin-Huxley Oscillatory Network
►Cpyclustering.nnet.hysteresis.hysteresis_dynamic	Represents output dynamic of hysteresis oscillatory network
Cpyclustering.gcolor.hysteresis.hysteresis_analyser	Performs analysis of output dynamic of the hysteresis oscillatory network to extract information about clusters or color allocation
Cpyclustering.nnet.hysteresis.hysteresis_visualizer	Visualizer of output dynamic of hysteresis oscillatory network
Cpyclustering.container.kdtree.kdtree	Represents KD Tree that is a space-partitioning data structure for organizing points in a k-dimensional space
Cpyclustering.container.kdtree.kdtree_text_visualizer	KD-tree text visualizer that provides service to diplay tree structure using text representation
Cpyclustering.cluster.kmeans.kmeans	Class implements K-Means clustering algorithm
Cpyclustering.cluster.kmeans.kmeans_observer	Observer of K-Means algorithm that is used to collect information about clustering process on each iteration of the algorithm
Cpyclustering.cluster.center_initializer.kmeans_plusplus_initializer	K-Means++ is an algorithm for choosing the initial centers for algorithms like K-Means or X-Means
Cpyclustering.cluster.kmeans.kmeans_visualizer	Visualizer of K-Means algorithm's results
Cpyclustering.cluster.kmedians.kmedians	Class represents clustering algorithm K-Medians
Cpyclustering.cluster.kmedoids.kmedoids	Class represents clustering algorithm K-Medoids
Cpyclustering.nnet.legion.legion_dynamic	Represents output dynamic of LEGION
Cpyclustering.nnet.legion.legion_parameters	Describes parameters of LEGION
►Cpyclustering.nnet.network	Common network description that consists of information about oscillators and connection between them
Cpyclustering.nnet.fsync.fsync_network	Model of oscillatory network that uses Landau-Stuart oscillator and Kuramoto model as a synchronization mechanism
Cpyclustering.nnet.hhn.hhn_network	Oscillatory Neural Network with central element based on Hodgkin-Huxley neuron model
►Cpyclustering.nnet.hysteresis.hysteresis_network	Hysteresis oscillatory network that uses relaxation oscillators that are represented by objective hysteresis neurons whose output in range [-1, +1]
Cpyclustering.gcolor.hysteresis.hysteresisgcolor	Class represents graph coloring algorithm based on hysteresis oscillatory network
Cpyclustering.nnet.legion.legion_network	Local excitatory global inhibitory oscillatory network (LEGION) that uses relaxation oscillator based on Van der Pol model
Cpyclustering.nnet.pcnn.pcnn_network	Model of oscillatory network that is based on the Eckhorn model
►Cpyclustering.nnet.sync.sync_network	Model of oscillatory network that is based on the Kuramoto model of synchronization
►Cpyclustering.cluster.syncnet.syncnet	Class represents clustering algorithm SyncNet
Cpyclustering.cluster.hsyncnet.hsyncnet	Class represents clustering algorithm HSyncNet
Cpyclustering.gcolor.sync.syncgcolor	Oscillatory network based on Kuramoto model with negative and positive connections for graph coloring problem
Cpyclustering.nnet.syncpr.syncpr	Model of phase oscillatory network for pattern recognition that is based on the Kuramoto model
Cpyclustering.container.kdtree.node	Represents node of KD-Tree
Cpyclustering.cluster.optics.optics	Class represents clustering algorithm OPTICS (Ordering Points To Identify Clustering Structure) with KD-tree optimization (ccore options is supported)
Cpyclustering.cluster.optics.optics_descriptor	Object description that used by OPTICS algorithm for cluster analysis
Cpyclustering.nnet.sync.order_estimator	Provides services to calculate order parameter and local order parameter that are used for synchronization level estimation
Cpyclustering.cluster.optics.ordering_analyser	Analyser of cluster ordering diagram
Cpyclustering.cluster.optics.ordering_visualizer	Cluster ordering diagram visualizer that represents dataset graphically as density-based clustering structure
Cpyclustering.nnet.pcnn.pcnn_dynamic	Represents output dynamic of PCNN (pulse-coupled neural network)
Cpyclustering.nnet.pcnn.pcnn_parameters	Parameters for pulse coupled neural network
Cpyclustering.nnet.pcnn.pcnn_visualizer	Visualizer of output dynamic of pulse-coupled neural network (PCNN)
Cpyclustering.cluster.center_initializer.random_center_initializer	Random center initializer is for generation specified amount of random of centers for specified data
Cpyclustering.cluster.rock.rock	Class represents clustering algorithm ROCK
Cpyclustering.cluster.silhouette.silhouette	Represents Silhouette method that is used interpretation and validation of consistency
Cpyclustering.cluster.silhouette.silhouette_ksearch	Represent algorithm for searching optimal number of clusters using specified K-algorithm (K-Means, K-Medians, K-Medoids) that is based on Silhouette method
Cpyclustering.nnet.som.som	Represents self-organized feature map (SOM)
Cpyclustering.nnet.som.som_parameters	Represents SOM parameters
Cpyclustering.cluster.somsc.somsc	Class represents simple clustering algorithm based on self-organized feature map
Cpyclustering.cluster.clique.spatial_block	Geometrical description of CLIQUE block in data space
Cpyclustering.cluster.bang.spatial_block	Geometrical description of BANG block in data space
►Cpyclustering.nnet.sync.sync_dynamic	Represents output dynamic of Sync
Cpyclustering.cluster.syncnet.syncnet_analyser	Performs analysis of output dynamic of the oscillatory network syncnet to extract information about cluster allocation
Cpyclustering.gcolor.sync.syncgcolor_analyser	Analyser of output dynamic of the oscillatory network syncgcolor
Cpyclustering.nnet.syncpr.syncpr_dynamic	Represents output dynamic of syncpr (Sync for Pattern Recognition)
►Cpyclustering.nnet.sync.sync_visualizer	Visualizer of output dynamic of sync network (Sync)
Cpyclustering.cluster.syncnet.syncnet_visualizer	Visualizer of output dynamic of oscillatory network 'syncnet' for cluster analysis
Cpyclustering.nnet.syncpr.syncpr_visualizer	Visualizer of output dynamic of syncpr network (Sync for Pattern Recognition)
Cpyclustering.nnet.syncsegm.syncsegm	Class represents segmentation algorithm syncsegm
Cpyclustering.nnet.syncsegm.syncsegm_analyser	Performs analysis of output dynamic of the double-layer oscillatory network 'syncsegm' to extract information about segmentation results
Cpyclustering.nnet.syncsegm.syncsegm_visualizer	Result visualizer of double-layer oscillatory network 'syncsegm'
Cpyclustering.cluster.syncsom.syncsom	Class represents clustering algorithm SYNC-SOM
Cpyclustering.cluster.xmeans.xmeans	Class represents clustering algorithm X-Means
►CIntEnum
Cpyclustering.cluster.agglomerative.type_link	Enumerator of types of link between clusters
Cpyclustering.cluster.ema.ema_init_type	Enumeration of initialization types for Expectation-Maximization algorithm
Cpyclustering.cluster.encoder.type_encoding	Enumeration of encoding types (index labeling, index list separation, object list separation)
Cpyclustering.cluster.silhouette.silhouette_ksearch_type	Defines algorithms that can be used to find optimal number of cluster using Silhouette method
Cpyclustering.cluster.xmeans.splitting_type	Enumeration of splitting types that can be used as splitting creation of cluster in X-Means algorithm
Cpyclustering.container.cftree.cfnode_type	Enumeration of CF-Node types that are used by CF-Tree
Cpyclustering.container.cftree.measurement_type	Enumeration of measurement types for CF-Tree
Cpyclustering.nnet.cnn.type_conn	Enumeration of connection types for Chaotic Neural Network
Cpyclustering.nnet.conn_represent	Enumerator of internal network connection representation between oscillators
Cpyclustering.nnet.conn_type	Enumerator of connection types between oscillators
Cpyclustering.nnet.initial_type	Enumerator of types of oscillator output initialization
Cpyclustering.nnet.solve_type	Enumerator of solver types that are used for network simulation
Cpyclustering.nnet.som.type_conn	Enumeration of connection types for SOM
Cpyclustering.nnet.som.type_init	Enumeration of initialization types for SOM
Cpyclustering.utils.graph.type_graph_descr	Enumeration of graph description
Cpyclustering.utils.metric.type_metric	Enumeration of supported metrics in the module for distance calculation between two points