A new data clustering approach: Generalized cellular automata

作者：Shuai Dianxun^*; Dong Yumin; Shuai Qing

来源：Information Systems, 2007, 32(7): 968-977.

DOI：10.1016/j.is.2006.10.002

• enterprise computing
• data clustering
• cellular automata
• parallel algorithm
• Markov process
• entropy
• stationary
• distribution

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.

• 出版日期2007-11
• 单位华东理工大学; 华中科技大学; 青岛理工大学