Title :
A Nonparametric Valley-Seeking Technique for Cluster Analysis
Author :
Koontz, Warren L.G. ; Fukunaga, Keinosuke
Author_Institution :
School of Electrical Engineering, Purdue University, Lafayette, Ind.; Bell Telephone Laboratories, Inc., Holmdel, N. J. 07733.
Abstract :
The problem of clustering multivariate observations is viewed as the replacement of a set of vectors with a set of labels and representative vectors. A general criterion for clustering is derived as a measure of representation error. Some special cases are derived by simplifying the general criterion. A general algorithm for finding the optimum classification with respect to a given criterion is derived. For a particular case, the algorithm reduces to a repeated application of a straightforward decision rule which behaves as a valley-seeking technique. Asymptotic properties of the procedure are developed. Numerical examples are presented for the finite sample case.
Keywords :
Animal structures; Clustering algorithms; History; Object detection; Pattern analysis; Pattern recognition; Statistical analysis; Testing; Clustering; clustering algorithms; clustering criteria; multivariate analysis; pattern recognition;
Journal_Title :
Computers, IEEE Transactions on
DOI :
10.1109/TC.1972.5008922
