NDLI: Self-Organizing Data Clustering: A Novel Stochastic Generalized Cellular Automata

Content Provider	IEEE Xplore Digital Library
Author	Dianxun Shuai Yuzhe Liu Ping Zhang
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Sci. & Eng., East China Univ. of Sci. & Tech. (Dianxun Shuai)
Abstract	A random generalized cellular automata (GCA) for self-organizing data clustering is proposed. Using GCA, we transform the data clustering into a stochastic process over the configuration space of cellular states on a GCA array. The proposed approach has many advantages in terms of the self-organizing clustering, insensitivity to noise, quality robustness to clustered data, suitability for high-dimensional and massive data sets, learning ability, and the easier hardware implementation with the VLSI systolic technology. The simulations and comparisons have shown the effectiveness and good performance of the proposed GCA approach to data clustering
Starting Page	3165
Ending Page	3170
File Size	6980242
Page Count	6
File Format	PDF
ISBN	1424404967
DOI	10.1109/ISIE.2006.296122
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-09
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stochastic processes Clustering algorithms Concurrent computing Cellular neural networks Aggregates Clustering methods Partitioning algorithms Organizing Noise robustness Hardware
Content Type	Text
Resource Type	Article

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Self-Organizing Data Clustering: A Novel Stochastic Generalized Cellular Automata