Metadata-Version: 2.1
Name: s-dbw
Version: 0.2.0
Summary: Compute the S_Dbw validity index
Home-page: https://github.com/alashkov83/S_Dbw
Author: Alexander Lashkov, Sergey Rubinsky, Polina Eistrikh-Heller
Author-email: alashkov83@gmail.com
Maintainer: Alexander Lashkov
License: MIT License
Description: # S_Dbw
        Compute the S_Dbw validity index  
        S_Dbw validity index is defined by equation:  
        ```S_Dbw = scatt + dens```  
        where scatt - means average scattering for clusters and dens - inter-cluster density.  
        Lower value -> better clustering.  
        
        Parameters
        ----------
        X : array-like, shape (``n_samples``, ``n_features``)  
            List of ``n_features``-dimensional data points. Each row corresponds
            to a single data point.  
        labels : array-like, shape (``n_samples``,)  
            Predicted labels for each sample (-1 - for noise).  
        centers_id : array-like, shape (``n_samples``,)  
            The center_id of each cluster's center. If None - cluster's center calculate automatically.  
        alg_noise : str,  
            Algorithm for recording noise points.  
            'comb' - combining all noise points into one cluster (default)  
            'sep' - definition of each noise point as a separate cluster  
            'bind' -  binding of each noise point to the cluster nearest from it  
            'filter' - filtering noise points
        metric : str,  
            The distance metric, can be ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, ‘correlation’,
            ‘cosine’, ‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’, ‘matching’, ‘minkowski’,
            ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘wminkowski’,
            ‘yule’. Default is ‘euclidean’.  
        
        Returns
        -------
        score : float  
            The resulting S_DBw score.  
        
        References:
        -----------
        [1] Clustering Validity Assessment: Finding the optimal partitioning of a data set  
            <https://pdfs.semanticscholar.org/dc44/df745fbf5794066557e52074d127b31248b2.pdf>  
        [2] Understanding of Internal Clustering Validation Measures  
            <http://datamining.rutgers.edu/publication/internalmeasures.pdf>
        
Keywords: clustering,cluster analysis,cluster validation
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Requires-Python: >=2.7
Description-Content-Type: text/markdown
