CDbw

Compute the S_Dbw validity index
S_Dbw validity index is defined by equation:

CDbw = compactness*cohesion*separation

Highest value -> better clustering.

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Installation:

pip install --upgrade cdbw

Usage:

from cdbw import CDbw
score = CDbw(X, labels, metric="euclidean", alg_noise='comb', 
     intra_dens_inf=False, s=3, multipliers=False)

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)
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’.
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
intra_dens_inf : bool,
    If False (default) CDbw index = 0 for cohesion or compactness - inf or nan.
s : int,
    Number of art representative points. (>2)
multipliers : bool,
    Format of output. False (default) - only CDbw index, True - tuple (compactness, cohesion, separation, CDbw)

Returns:

cdbw : float,
    The resulting CDbw validity index.

References:

1. M. Halkidi and M. Vazirgiannis, “A density-based cluster validity approach using multi-representatives” Pattern Recognition Letters 29 (2008) 773–786.