Skip to main content

Compute the CDbw validity index

Project description


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

CDbw = compactness*cohesion*separation

Highest value -> better clustering.


pip install --upgrade cdbw


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


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’,
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)


cdbw : float,
    The resulting CDbw validity index.


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

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

cdbw-0.2.tar.gz (8.6 kB view hashes)

Uploaded source

Built Distribution

cdbw-0.2-py3-none-any.whl (8.0 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page