DocumentCode :
3374745
Title :
A Novel Stochastic Generalized Cellular Automata For Self-Organizing Data Clustering
Author :
Shuai, Dianxun ; Shuai, Qing ; Huang, Liangjun ; Liu, Yuzhe ; Dong, Yuming
Author_Institution :
Dept. of Comput. Sci. & Eng., East China Univ. of Sci. & Technol., Shanghai
Volume :
2
fYear :
2006
fDate :
20-24 June 2006
Firstpage :
724
Lastpage :
730
Abstract :
This paper is devoted to novel stochastic generalized cellular automata (GCA) for self-organizing data clustering. The GCA transforms the data clustering process into a stochastic process over the configuration space in the GCA array. The proposed approach is characterized by the self-organizing clustering and many advantages in terms of the insensitivity to noise, quality robustness to clustered data, suitability for high-dimensional and massive data sets, the 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
Keywords :
cellular automata; data mining; pattern clustering; stochastic automata; stochastic processes; GCA array; VLSI systolic technology; data mining; self-organizing data clustering; stochastic generalized cellular automata; Clustering algorithms; Clustering methods; Computer networks; Concurrent computing; Hardware; Noise robustness; Space technology; Stochastic processes; Stochastic resonance; Very large scale integration;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer and Computational Sciences, 2006. IMSCCS '06. First International Multi-Symposiums on
Conference_Location :
Hanzhou, Zhejiang
Print_ISBN :
0-7695-2581-4
Type :
conf
DOI :
10.1109/IMSCCS.2006.161
Filename :
4673792
Link To Document :
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