AN INVESTIGATION OF MOUNTAIN METHOD CLUSTERING FOR LARGE DATA SETS

The Mountain Method of clustering was introduced by Yager and Filev an d refined for practical use by Chiu. The approach is based on density estimation in feature space with the highest peak extracted as a clust er center and a new density estimation created for extraction of the n ext 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 Toolbox. In this paper, we develop an alternate im plementation 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 doma in. Comparisons with the Matlab implementation show that our new appro ach is considerably more practical in terms of the time required to cl uster, as well as better at producing partitions of the data that corr espond to those expected. Comparisons are also made to the fuzzy c-mea ns clustering algorithm, which show that our improved mountain method is a viable competitor, producing excellent partitions of large data s ets.