pyclustering 0.10.0.1 library is a collection of clustering algorithms and methods, oscillatory networks, etc.

GENERAL CHANGES:

• Metadata of the library is updated.
  See: no reference

• Supported command test for setup.py script (Python: pyclustering).
  See: #607

• Introduced parameter random_seed for algorithms/models to control the seed of the random functionality: kmeans++, random_center_initializer, ga, gmeans, xmeans, som, somsc, elbow, silhouette_ksearch (Python: pyclustering.cluster; C++: pyclustering.clst).
  See: #578

• Introduced parameter k_max to G-Means algorithm to use it as an optional stop condition for the algorithm (Python: pyclustering.cluster.gmeans; C++: pyclustering::clst::gmeans).
  See: #602

• Implemented method save() for cluster_visualizer and cluster_visualizer_multidim to save visualization to file (Python: pyclustering.cluster).
  See: #601

• Optimization of CURE algorithm using balanced KD-tree (Python: pyclustering.cluster.cure; C++: pyclustering::clst::cure).
  See: #589

• Optimization of OPTICS algorithm using balanced KD-tree (Python: pyclustering.cluster.optics; C++: pyclustering::clst::optics).
  See: #588

• Optimization of DBSCAN algorithm using balanced KD-tree (Python: pyclustering.cluster.dbscan; C++: pyclustering::clst::dbscan).
  See: #587

• Implemented new optimized balanced KD-tree kdtree_balanced (Python: pyclustering.cluster.kdtree; C++: pyclustering::container::kdtree_balanced).
  See: #379

• Implemented KD-tree graphical visualizer kdtree_visualizer for KD-trees with 2-dimensional data (Python: pyclustering.container.kdtree).
  See: #586

• Updated interface of each clustering algorithm in C/C++ pyclustering cluster_data is substituted by concrete classes (C++ pyclustering::clst).
  See: #577

CORRECTED MAJOR BUGS:

• Bug with wrong data type for scores in Silhouette K-search algorithm in case of using C++ (Python: pyclustering.cluster.silhouette).
  See: #606

• Bug with a random distribution in the random center initializer (Python: pyclustering.cluster.center_initializer).
  See: #573

• Bug with incorrect converting Index List and Object List to Labeling when clusters do not contains one or more points from an input data (Python pyclustering.cluster.encoder).
  See: #596

• Bug with an exception in case of using user-defined metric for K-Means algorithm (Python pyclustering.cluster.kmeans).
  See: #600

• Memory leakage in the interface between python and C++ pyclustering library in case of CURE algorithm usage (C++ pyclustering).
  See: #581