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        "author": "Sergey Rubinsky, Alexander Lashkov, Polina Eistrikh-Heller",
        "author_email": "alashkov83@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved",
            "Operating System :: MacOS",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
            "Operating System :: Unix",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.4",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
        ],
        "description": "# CDbw\nCompute the S_Dbw validity index  \nS_Dbw validity index is defined by equation:\n##### CDbw = compactness\\*cohesion*separation\n**Highest value -> better clustering.**\n______________________________________________\n\n#### Installation:\n\n```shell\npip install --upgrade cdbw\n```\n\n### Usage:\n\n```python\nfrom cdbw import CDbw\nscore = CDbw(X, labels, metric=\"euclidean\", alg_noise='comb', \n     intra_dens_inf=False, s=3, multipliers=False)\n\n```\n\n### Parameters:\n    X : array-like, shape (n_samples, n_features)\n        List of n_features-dimensional data points. Each row corresponds\n        to a single data point.\n    labels : array-like, shape (n_samples,)\n        Predicted labels for each sample.  (-1 - for noise)\n    metric : str,\n        The distance metric, can be \u2018braycurtis\u2019, \u2018canberra\u2019, \u2018chebyshev\u2019, \u2018cityblock\u2019, \u2018correlation\u2019,\n        \u2018cosine\u2019, \u2018dice\u2019, \u2018euclidean\u2019, \u2018hamming\u2019, \u2018jaccard\u2019, \u2018kulsinski\u2019, \u2018mahalanobis\u2019, \u2018matching\u2019, \u2018minkowski\u2019,\n        \u2018rogerstanimoto\u2019, \u2018russellrao\u2019, \u2018seuclidean\u2019, \u2018sokalmichener\u2019, \u2018sokalsneath\u2019, \u2018sqeuclidean\u2019, \u2018wminkowski\u2019,\n        \u2018yule\u2019.\n    alg_noise : str,\n        Algorithm for recording noise points.\n        'comb' - combining all noise points into one cluster (default)\n        'sep' - definition of each noise point as a separate cluster\n        'bind' -  binding of each noise point to the cluster nearest from it\n        'filter' - filtering noise points\n    intra_dens_inf : bool,\n        If False (default) CDbw index = 0 for cohesion or compactness - inf or nan.\n    s : int,\n        Number of art representative points. (>2)\n    multipliers : bool,\n        Format of output. False (default) - only CDbw index, True - tuple (compactness, cohesion, separation, CDbw)\n\n### Returns:\n    cdbw : float,\n        The resulting CDbw validity index.\n\nReferences:\n-----------\n1. M. Halkidi and M. Vazirgiannis, \u201cA density-based cluster validity approach using multi-representatives\u201d\n        Pattern Recognition Letters 29 (2008) 773\u2013786.\n\n\n",
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        "home_page": "https://github.com/alashkov83/CDbw",
        "keywords": "clustering,cluster analysis,cluster validation",
        "license": "MIT License",
        "maintainer": "Alexander Lashkov",
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        "name": "cdbw",
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        "summary": "Compute the CDbw validity index",
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