generalizedDunn_fast: Generalized Dunn Index

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

Description

Calculates a generalized version of the Dunn Index, allowing an arbitrary measure of cluster separation (which goes to the numerator and is minimized) and an arbitrary measure of cluster compactness (which goes to the denominator and is maximized over all clusters). Dunn used the highly outlier-prone single linkage measure for separation and complete linkage for compactness. Higher values indicate better clustering quality.

Usage

generalizedDunn_fast(interClusterDistances, intraClusterDistances)

Arguments

interClusterDistances	A symmetric 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 Dunn Index. Could be Inf or NaN if there are clusters with a distance of zero between them (which is a bad clustering result).

References

Bezdek, J. C. & Pal, N. R. (1998). Some new indexes of cluster validity. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 28(3), 301–315.

Dunn, J. C. (1973). A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3(3), 32–57.

See Also

Other Internal Cluster Validity Indices: generalizedDB_fast, iGeneralizedDB_fast

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