pyclustering 0.8.2

pyclustering 0.8.2 library is a collection of clustering algorithms and methods, oscillatory networks, neural networks, etc.

GENERAL CHANGES:

• Implemented Silhouette method and Silhouette KSearcher to find out proper amount of clusters (pyclustering.cluster.silhouette). See: https://github.com/annoviko/pyclustering/issues/416

• Introduced new 'return_index' parameter for kmeans_plus_plus and random_center_initializer algorithms (method 'initialize') to initialize initial medoids (pyclustering.cluster.center_initializer). See: https://github.com/annoviko/pyclustering/issues/421

• Display warning instead of throwing error if matplotlib or Pillow cannot be imported (MAC OS X problems). See: https://github.com/annoviko/pyclustering/issues/455

• Implemented Random Center Initializer for CCORE (ccore.clst.random_center_initializer). See: no reference.

• Implemented Elbow method to find out proper amount of clusters in dataset (pyclustering.cluster.elbow, ccore.clst.elbow). See: https://github.com/annoviko/pyclustering/issues/416

• Introduced new method 'get_optics_objects' for OPTICS algorithm to obtain detailed information about ordering (pyclustering.cluster.optics, ccore.clst.optics). See: https://github.com/annoviko/pyclustering/issues/464

• Added new clustering answers for SAMPLE SIMPLE data collections (pyclustering.samples). See: https://github.com/annoviko/pyclustering/issues/459

• Implemented multidimensional cluster visualizer (pyclustering.cluster). See: https://github.com/annoviko/pyclustering/issues/450

• Parallel optimization of K-Medoids algorithm (ccore.clst.kmedoids). See: https://github.com/annoviko/pyclustering/issues/447

• Parallel optimization of K-Means and X-Means (that uses K-Means) algorithms (ccore.clst.kmeans, ccore.clst.xmeans). See: https://github.com/annoviko/pyclustering/issues/451

• Introduced new threshold parameter 'amount of block points' to BANG algorithm to allocate outliers more precisely (pyclustering.cluster.bang). See: https://github.com/annoviko/pyclustering/issues/446

• Optimization of conveying results from C++ to Python for K-Medians and K-Medoids (pyclustering.cluster.kmedoids, pyclustering.cluster.kmedians). See: https://github.com/annoviko/pyclustering/issues/445

• Implemented cluster generator (pyclustering.cluster.generator). See: https://github.com/annoviko/pyclustering/issues/444

• Implemented BANG animator to render animation of clustering process (pyclustering.cluster.bang). See: https://github.com/annoviko/pyclustering/issues/442

• Optimization of CURE algorithm by using Euclidean Square distance (pyclustering.cluster.cure, ccore.clst.cure). See: https://github.com/annoviko/pyclustering/issues/439

• Supported numpy.ndarray points in KD-tree (pyclustering.container.kdtree). See: https://github.com/annoviko/pyclustering/issues/438

CORRECTED MAJOR BUGS:

• Bug with clustering failure in case of non-numpy user defined metric for K-Means algorithm (pyclustering.cluster.kmeans). See: https://github.com/annoviko/pyclustering/issues/471

• Bug with animation of correlation matrix in case of new versions of matplotlib (pyclustering.nnet.sync). See: no reference.

• Bug with SOM and pickle when it was not possible to store and load network using pickle (pyclustering.nnet.som). See: https://github.com/annoviko/pyclustering/issues/456

• Bug with DBSCAN when points are marked as a noise (pyclustering.cluster.dbscan). See: https://github.com/annoviko/pyclustering/issues/462

• Bug with randomly enabled connection weights in case of SyncNet based algorithms using CCORE interface (pyclustering.nnet.syncnet). See: https://github.com/annoviko/pyclustering/issues/452

• Bug with calculation weighted connection for Sync based clustering algorithms in C++ implementation (ccore.nnet.syncnet). See: no reference

• Bug with failure in case of numpy.ndarray data type in python part of CURE algorithm (pyclustering.cluster.cure). See: https://github.com/annoviko/pyclustering/issues/438

• Bug with BANG algorithm with empty dimensions - when data contains column with the same values (pyclustering.cluster.bang). See: https://github.com/annoviko/pyclustering/issues/449