Title :
A Cluster Separation Measure
Author :
Davies, David L. ; Bouldin, Donald W.
Author_Institution :
Department of Electrical Engineering, University of Tennessee, Knoxville, TN 37916; 17 C Downey Drive, Manchester, CT 06040.
fDate :
4/1/1979 12:00:00 AM
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 :
Algorithm design and analysis; Clustering algorithms; Data analysis; Density measurement; Dispersion; Humans; Missiles; Multidimensional systems; Partitioning algorithms; Performance analysis; Cluster; data partitions; multidimensional data analysis; parametric clustering; partitions; similarity measure;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.1979.4766909
