A Cluster Separation Measure

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.

Keywords

Measure (data warehouse)Cluster (spacecraft)Similarity measureComputer scienceData miningArtificial intelligencePattern recognition (psychology)Similarity (geometry)Function (biology)Separation (statistics)MathematicsMachine learning

Affiliated Institutions

University of Tennessee at Knoxville US

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Publication Info

Year: 1979
Type: article
Volume: PAMI-1
Issue: 2
Pages: 224-227
Citations: 8293
Access: Closed

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APA Style

                            
                                    David L. Davies, 
                                
                                    Donald W. Bouldin
                                
                            (1979). 
                            A Cluster Separation Measure. 
                            IEEE Transactions on Pattern Analysis and Machine Intelligence
                            , PAMI-1
                            (2)
                            , 224-227.
                            https://doi.org/10.1109/tpami.1979.4766909

Identifiers

DOI: 10.1109/tpami.1979.4766909