Particle Swarm Optimization Based Clustering: A Comparison of Different Cluster Validity Indices
Most of clustering algorithms based on natural computation aim to find the proper partition of data to be processed by optimizing certain criteria, socalled as cluster validity index, which must be effective and can reflect a similarity measure among objects properly. Up to now, four typical cluster validity indices such as Euclid distance-based PBM index, the kernel function induced CS measure, Point Symmetry (PS) distance-based index,Manifold Distance (MD) induced index have been proposed. But, there is not a detailed comparison among these indexes. In this paper, we design a particle swarm optimization based clustering algorithm, in which, four different cluster validity index above mentioned are used as the fitness of a particle respectively. By applying the proposed algorithm to a number of artificial synthesized data and UCI data, the performance of different validity indices are compared in terms of clustering accuracy and robustness at length.
particle swarm optimization clustering cluster validity PBM index CS measure point symmetry distance manifold distance
Ruochen Liu Xiaojuan Sun Licheng Jiao
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xian, 710071
国际会议
无锡
英文
66-72
2010-09-17（万方平台首次上网日期，不代表论文的发表时间）