An investigation of mountain method clustering for large data sets

Authors:
Robert P. Velthuizen;Lawrence O. Hall;Laurence P. Clarke;Martin L. Silbiger
Affiliations:
Department of Radiology, College of Medicine, University of South Florida and the H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, U.S.A.;College of Engineering, University of South Florida, Tampa, Florida, U.S.A.;Department of Radiology, College of Medicine, University of South Florida and the H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, U.S.A.;Department of Radiology, College of Medicine, University of South Florida and the H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, U.S.A.
Venue:
Pattern Recognition
Year:
1997

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Abstract

The Mountain Method of clustering was introduced by Yager and Filev and refined for practical use by Chiu. The approach is based on density estimation in feature space with the highest peak extracted as a cluster center and a new density estimation created for extraction of the next cluster center. The process is repeated until a stopping condition is met. The Chiu version of this approach has been implemented in the Matlab Fuzzy Logic Tool@?. In this paper, we develop an alternate implementation that allows large data sets to be processed effectively. Methods to set the parameters required by the algorithm are also given. Magnetic resonance images of the human brain are used as a test domain. Comparisons with the Matlab implementation show that our new approach is considerably more practical in terms of the time required to cluster, as well as better at producing partitions of the data that correspond to those expected. Comparisons are also made to the fuzzy c-means clustering algorithm, which show that our improved mountain method is a viable competitor, producing excellent partitions of large data sets.