generalizedDB_fast: Generalized Davies-Bouldin Index

In Jakob-Bach/FastTSDistances: Fast dissimilarity computations for time series

Description

Calculates a generalized version of the Davies-Bouldin Index for internal cluster validation. The index is expressed by a ratio of cluster compactness and cluster separation, summed and averaged over all clusters. This generalized method does not define the concrete way to compute distances within clusters and between clusters, but simply takes these distances as input to compute the Davies-Bouldin Index. Lower values indicate better clustering quality.

Usage

generalizedDB_fast(interClusterDistances, intraClusterDistances)

Arguments

interClusterDistances	A matrix representing the distances between clusters (separation), with the number of rows/columns equal to the number of clusters.
intraClusterDistances	A vector representing the distances within a cluster (compactness), with its length equal to the number of clusters.

Value

The Generalized Davies-Bouldin Index. Could be Inf or NaN if there are clusters with a distance of zero between them (which is a bad clustering result).

References

Davies, D. L. & Bouldin, D. W. (1979). A cluster separation measure. IEEE transactions on pattern analysis and machine intelligence, 1(2), 224–227.

See Also

Other Internal Cluster Validity Indices: generalizedDunn_fast, iGeneralizedDB_fast

Jakob-Bach/FastTSDistances documentation built on May 13, 2019, 1:15 p.m.