Compute the S_Dbw validity index
Project 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
Project details
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