Title :
Cellular Automata for Self-Organizing Data Clustering
Author :
Shuai, Dianxun ; Xu, Li D. ; Zhang, Bin ; Dong, Yumin
Author_Institution :
East China Univ. of Sci. & Technol., Shanghai
Abstract :
A data clustering method based on the generalized cellular automata (GCA) is presented. The stochastic process over a GCA array is used to realize the self-organization of data clusters for the given data sets. Due to its particular stochastic characteristics, GCA can deal with dynamical data, noise data, multi-type data, asymmetrically distributed data, multi-shaped-cluster data, high-dimensional data and massive data sets. The GCA-based data clustering also has advantages over other sequential methods in terms of the high parallelism, the learning ability, and the easier hardware implementation with the VLSI systolic technology.
Keywords :
cellular automata; pattern clustering; stochastic processes; VLSI systolic technology; asymmetrically distributed data; data clustering method; dynamical data; generalized cellular automata; high-dimensional data; massive data sets; multishaped-cluster data; multitype data; noise data; self-organizing data clustering; sequential methods; Clustering algorithms; Clustering methods; Cybernetics; Hardware; Iterative algorithms; Iterative methods; Partitioning algorithms; Shape; Stochastic processes; Stochastic resonance;
Conference_Titel :
Systems, Man and Cybernetics, 2006. SMC '06. IEEE International Conference on
Conference_Location :
Taipei
Print_ISBN :
1-4244-0099-6
Electronic_ISBN :
1-4244-0100-3
DOI :
10.1109/ICSMC.2006.384639
