文献类型：会议论文

英文题名：Self-organizing data clustering based on quantum entanglement model

作者：Shuai, Dianxun[1];Liu, Yuzhe[1];Qing Shuai[2];Huang, Liangjun[1];Dong, Yuming[3]

机构：[1]E China Univ Sci & Tech, Dept Comp Sci & Engn, Shanghai, Peoples R China;[2]Huazhong Univ Sci & Technol, Dept Sociol, Wuhan 430074, Peoples R China;[3]Qingdao Technol Univ, Comp Network Ctr, Qingdao 266033, Peoples R China

会议论文集：1st International Multi Symposium on Computer and Computational Sciences

会议日期：JUN 20-24, 2006

会议地点：Hangzhou, PEOPLES R CHINA

语种：英文

外文关键词：Computer simulation - Data structures - Data transfer - Quantum cryptography - Random processes

摘要：Most of currently used approaches to data clustering are not qualified to quickly cluster a high-dimensional large-scale database This paper is devoted to a novel generalized quantum particle model (GQPM) to data self-organizing clustering. The GQPM approach transforms the data clustering process into a stochastic process of particle motion, collision and quantum entanglement on a particle array. In comparison With the GPM clustering method we have proposed before, the GQPM has much faster speed and higher quality for clustering. GQPM is also characterized by the self-organizing clustering and has advantages in terms of the insensitivity to noise, the quality robustness to clustered data, the learning ability, the suitability for high-dimensional multi-shape large-scale data sets. The simulations and comparisons have shown the effectiveness and good performance of the proposed GQPM approach to data clustering.