Title :
Self-organizing mountain method for clustering
Author :
Wu, Chih-Wen ; Chen, Jin-Lian ; Wang, Jung-Hua
Author_Institution :
Dept. of Electr. Eng., Nat. Taiwan Ocean Univ., Keelung, Taiwan
Abstract :
A self-organizing mountain method (SOMM) is presented. SOMM incorporates the Possibilistic C-Means (PCM) technique and the concept of the mountain method to perform clustering. By self-organizing we mean that parameters are data-driven, the terrain of each cluster (or mountain) is estimated, the precise center of each cluster and the terminating condition are determined by the input nature. In addition, SOMM is robust even when a large number of outliers/noises is presented. The simulation results show that the robust clustering can be obtained for various Gaussian clusters and uniform clusters, respectively
Keywords :
fuzzy logic; pattern clustering; statistical analysis; Gaussian clusters; Possibilistic C-Means; SOMM; clustering; fuzzy clustering; mode-seeking algorithm; mountain method; robust clustering; self-organizing mountain method; simulation results; uniform clusters; Clustering algorithms; Iterative algorithms; Iterative methods; Oceans; Partitioning algorithms; Phase change materials; Prototypes; Robustness; Sea measurements; Shape measurement;
Conference_Titel :
Systems, Man, and Cybernetics, 2001 IEEE International Conference on
Conference_Location :
Tucson, AZ
Print_ISBN :
0-7803-7087-2
DOI :
10.1109/ICSMC.2001.972922
