A new data clustering approach: Generalized cellular automata

Authors:
Dianxun Shuai;Yumin Dong;Qing Shuai
Affiliations:
East China University of Science and Technology, Shanghai 200237, China;Department of Sociology, Huazhong University of Science and Technology, Wuhan 430074, China;Computer Network Center, Qingdao Technological University, Qingdao 266033, China
Venue:
Information Systems
Year:
2007

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Abstract

This paper is devoted to a novel stochastic generalized cellular automata (GCA) for self-organizing data clustering in enterprise computing. The GCA transforms the data clustering process into a stochastic process over the configuration space on a GCA array. The GCA-based approach to data clustering has many advantages in terms of the real-time performance and the ability to effectively deal with a variety of data sets, including noise data, dynamical data, multi-type and multi-distribution data, high-dimensional and large-scale data set. The GCA clustering approach also has the learning ability, and the better feasibility for hardware implementation with VLSI systolic technology. The simulations and comparisons have shown the effectiveness of the proposed GCA for the data clustering in enterprise computing.