sklearn.metrics.davies_bouldin_score

sklearn.metrics.davies_bouldin_score(X, labels) [source]

Computes the Davies-Bouldin score.

The score is defined as the ratio of within-cluster distances to between-cluster distances.

Read more in the User Guide.

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.
Returns:	score: float The resulting Davies-Bouldin score.

References

[1]	Davies, David L.; Bouldin, Donald W. (1979). “A Cluster Separation Measure”. IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI-1 (2): 224-227