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A Cluster Separation Measure

IEEE Transactions on Pattern Analysis and Machine Intelligence · 1979 · Vol. PAMI-1(2) · pp. 224–227

David L. Davies✉(University of Tennessee at Knoxville)Donald W. Bouldin(University of Tennessee at Knoxville)

Abstract

A measure is presented which indicates the similarity of clusters which are assumed to have a data density which is a decreasing function of distance from a vector characteristic of the cluster. The measure can be used to infer the appropriateness of data partitions and can therefore be used to compare relative appropriateness of various divisions of the data. The measure does not depend on either the number of clusters analyzed nor the method of partitioning of the data and can be used to guide a cluster seeking algorithm.

Advanced Clustering Algorithms ResearchMeasure (data warehouse)Cluster (spacecraft)Similarity measureComputer scienceData miningArtificial intelligencePattern recognition (psychology)Similarity (geometry)Function (biology)Separation (statistics)

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Validity index for crisp and fuzzy clusters