pyclustering 0.9.0 release

pyclustering 0.9.0 library is a collection of clustering algorithms and methods, oscillatory networks, neural networks, etc.

GENERAL CHANGES:

• CCORE (pyclustering core) is supported for MacOS.
  See: #486

• Introduced parallel Fuzzy C-Means algorithm (pyclustering.cluster.fcm, ccore.clst.fcm).
  See: #386

• Introduced new 'itermax' parameter for K-Means, K-Medians, K-Medoids algorithm to control maximum amount of iterations (pyclustering.cluster, ccore.clst).
  See: #496

• Implemented Silhouette and Silhouette K-Search algorithm for CCORE (ccore.clst.silhouette, ccore.clst.silhouette_ksearch).
  See: #490

• Implemented CLIQUE algorithms (pyclustering.cluster.clique, ccore.clst.clique).
  See: #381

• Introduced new distance metrics: Canberra and Chi Square (pyclustering.utils.metric, ccore.utils.metric).
  See: #482

• Optimization of CURE algorithm (C++ implementation) by using heap (multiset) instead of list to store clusters in queue (ccore.clst.cure).
  See: #479

CORRECTED MAJOR BUGS:

• Bug with crossover mask generation for genetic clustering algorithm (pyclustering.cluster.ga).
  See: #474

• Bug with hanging of K-Medians algorithm for some cases when algorithm is initialized by wrong amount of centers (ccore.clst.kmedians).
  See: #498

• Bug with incorrect center initialization, when the same point can be placed to result more than once (pyclustering.cluster.center_initializer, ccore.clst.kmeans_plus_plus).
  See: #497

• Bug with incorrect clustering in case of CURE python implementation when clusters are allocated incorrectly (pyclustering.cluster.cure).
  See: #483

• Bug with incorrect distance calculation for kmeans++ in case of index representation for centers (pyclustering.cluster.center_initializer).
  See: #485