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pyclustering 0.8.2 release
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@annoviko annoviko released this 19 Nov 11:38
· 9 commits to 0.8.2.rel since this release
0.8.2
3af9c03

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: #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: #421

  • Display warning instead of throwing error if matplotlib or Pillow cannot be imported (MAC OS X problems).
    See: #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: #416

  • Introduced new method 'get_optics_objects' for OPTICS algorithm to obtain detailed information about ordering (pyclustering.cluster.optics, ccore.clst.optics).
    See: #464

  • Added new clustering answers for SAMPLE SIMPLE data collections (pyclustering.samples).
    See: #459

  • Implemented multidimensional cluster visualizer (pyclustering.cluster).
    See: #450

  • Parallel optimization of K-Medoids algorithm (ccore.clst.kmedoids).
    See: #447

  • Parallel optimization of K-Means and X-Means (that uses K-Means) algorithms (ccore.clst.kmeans, ccore.clst.xmeans).
    See: #451

  • Introduced new threshold parameter 'amount of block points' to BANG algorithm to allocate outliers more precisely (pyclustering.cluster.bang).
    See: #446

  • Optimization of conveying results from C++ to Python for K-Medians and K-Medoids (pyclustering.cluster.kmedoids, pyclustering.cluster.kmedians).
    See: #445

  • Implemented cluster generator (pyclustering.cluster.generator).
    See: #444

  • Implemented BANG animator to render animation of clustering process (pyclustering.cluster.bang).
    See: #442

  • Optimization of CURE algorithm by using Euclidean Square distance (pyclustering.cluster.cure, ccore.clst.cure).
    See: #439

  • Supported numpy.ndarray points in KD-tree (pyclustering.container.kdtree).
    See: #438

CORRECTED MAJOR BUGS:

  • Bug with clustering failure in case of non-numpy user defined metric for K-Means algorithm (pyclustering.cluster.kmeans).
    See: #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: #456

  • Bug with DBSCAN when points are marked as a noise (pyclustering.cluster.dbscan).
    See: #462

  • Bug with randomly enabled connection weights in case of SyncNet based algorithms using CCORE interface (pyclustering.nnet.syncnet).
    See: #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: #438

  • Bug with BANG algorithm with empty dimensions - when data contains column with the same values (pyclustering.cluster.bang).
    See: #449

Assets 3