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pyclustering 0.10.0.1 release
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@annoviko annoviko released this 17 Aug 09:21
· 1 commit to 0.10.0.rel since this release
0.10.0.1
ae3db69

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

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