pyclustring is a python data mining library
Project description
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 pyclustering@yandex.ru.
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);
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