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

Volume

4

fYear

2006

fDate

8-11 Oct. 2006

Firstpage

3371

Lastpage

3376

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ICSMC.2006.384639

Filename

4274403