CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 1979 vol.1 Issue No.02 - February
Issue No.02 - February (1979 vol.1)
David L. Davies , Department of Electrical Engineering, University of Tennessee, Knoxville, TN 37916; 17 C Downey Drive, Manchester, CT 06040.
Donald W. Bouldin , Department of Electrical Engineering, University of Tennessee, Knoxville, TN 37916.
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.
Dispersion, Density measurement, Algorithm design and analysis, Clustering algorithms, Partitioning algorithms, Multidimensional systems, Data analysis, Performance analysis, Humans, Missiles,similarity measure, Cluster, data partitions, multidimensional data analysis, parametric clustering, partitions
David L. Davies, Donald W. Bouldin, "A Cluster Separation Measure", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.1, no. 2, pp. , February 1979, doi:10.1109/TPAMI.1979.4766909