pyclustering 0.7.2

pyclustring is a python data mining library

PyClustering

pyclustering is a Python, C++ data mining library (clustering algorithm, oscillatory networks, neural networks). The library provides Python and C++ implementations (via CCORE library) of each algorithm or model. CCORE library is a part of pyclustering and supported only for 64-bit Linux and 64-bit Windows operating systems.

Dependencies

Required packages: scipy, matplotlib, numpy, PIL

Python version: >=3.4 (64-bit)

C++ version: >= 14 (64-bit)

Installation

Installation using pip3 tool:

$ pip3 install pyclustering

Manual installation from official repository:

# get sources of the pyclustering library, for example, from repository
$ mkdir pyclustering
$ cd pyclustering/
$ git clone https://github.com/annoviko/pyclustering.git .

# compile CCORE library (core of the pyclustering library).
$ cd pyclustering/ccore
$ make ccore

# return to parent folder of the pyclustering library
cd ../

# add current folder to python path
PYTHONPATH=`pwd`
export PYTHONPATH=${PYTHONPATH}

Proposals, Questions, Bugs

In case of any questions, proposals or bugs related to the pyclustering please contact to [e-mail].

Issue tracker: https://github.com/annoviko/pyclustering

Library Content

Clustering algorithms (module pyclustering.cluster):

• Agglomerative (pyclustering.cluster.agglomerative);

• BIRCH (pyclustering.cluster.birch);

• CLARANS (pyclustering.cluster.clarans);

• CURE (pyclustering.cluster.cure);

• DBSCAN (pyclustering.cluster.dbscan);

• EMA (pyclustering.cluster.ema);

• GA (Genetic Algorithm) (pyclustering.cluster.ga);

• HSyncNet (pyclustering.cluster.hsyncnet);

• K-Means (pyclustering.cluster.kmeans);

• K-Means++ (pyclustering.cluster.center_initializer);

• K-Medians (pyclustering.cluster.kmedians);

• K-Medoids (PAM) (pyclustering.cluster.kmedoids);

• OPTICS (pyclustering.cluster.optics);

• ROCK (pyclustering.cluster.rock);

• SOM-SC (pyclustering.cluster.somsc);

• SyncNet (pyclustering.cluster.syncnet);

• Sync-SOM (pyclustering.cluster.syncsom);

• X-Means (pyclustering.cluster.xmeans);

Oscillatory networks and neural networks (module pyclustering.nnet):

• Oscillatory network based on Hodgkin-Huxley model (pyclustering.nnet.hhn);

• fSync: Oscillatory Network based on Landau-Stuart equation and Kuramoto model (pyclustering.nnet.fsync);

• Hysteresis Oscillatory Network (pyclustering.nnet.hysteresis);

• LEGION: Local Excitatory Global Inhibitory Oscillatory Network (pyclustering.nnet.legion);

• PCNN: Pulse-Coupled Neural Network (pyclustering.nnet.pcnn);

• SOM: Self-Organized Map (pyclustering.nnet.som);

• Sync: Oscillatory Network based on Kuramoto model (pyclustering.nnet.sync);

• SyncPR: Oscillatory Network based on Kuramoto model for pattern recognition (pyclustering.nnet.syncpr);

• SyncSegm: Oscillatory Network based on Kuramoto model for image segmentation (pyclustering.nnet.syncsegm);

Graph Coloring Algorithms (module pyclustering.gcolor):

• DSATUR (pyclustering.gcolor.dsatur);

• Hysteresis Oscillatory Network for graph coloring (pyclustering.gcolor.hysteresis);

• Sync: Oscillatory Network based on Kuramoto model for graph coloring (pyclustering.gcolor.sync);

Travelling Salesman Problem Algorithms (module pyclustering.tsp):

• AntColony (pyclustering.tsp.antcolony);

Containers (module pyclustering.container):

• CF-Tree (pyclustering.container.cftree);

• KD-Tree (pyclustering.container.kdtree);