文献类型：会议论文

英文题名：Self-organizing data clustering: A novel quantum particle approach

作者：Shuai, Dianxim[1];Zhang, Ping[2];Huang, Liangjun[1]

机构：[1]EAST China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[2]Xidian Univ, Inst Intelligent Informat Proc, Xian, Peoples R China

会议论文集：IEEE International Symposium on Industrial Electronics

会议日期：JUL 09-13, 2006

会议地点：Montreal, CANADA

语种：英文

外文关键词：Clustering algorithms - Quantum entanglement - Equivalence classes - Cluster analysis - Stochastic systems

摘要：A novel generalized quantum particle model (GQPM) is presented for data self-organizing clustering.(1) Using GQPM we transform the data clustering into a stochastic process of partitioning equivalence classes of particles under the quantum entanglement relation. The GQPM approach has much faster clustering speed and higher clustering quality than the nonquantum particle model GPM and GCA we proposed before. 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.