Particle Swarm Optimization for Dynamic Clustering

• Main Authors: Shu-Chen Yin, 殷淑貞
• Other Authors: Yu-Cheng Kao
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/34944364571653171729

碩士 === 大同大學 === 資訊經營學系(所) === 96 === Data clustering, one of the major research technologies of data mining, is the process of grouping together similar multi-dimensional data vectors into a number of clusters. The process of data clustering needs to consider the number of clusters and the result of clusters. The natural number of clusters will influence the final clustering result. How to find the optimal number of clusters becomes an important issue. In this research, the author develops a novel dynamic clustering method, called Particle Swarm Optimization for Dynamic Clustering (PSODC), to cluster a dataset without setting the cluster number in advance. PSODC consists of two stages: evolution of optimal cluster numbers and data clustering in each sub-swarm representing a specified cluster number. In the first stage, the particles in a sub-swarm randomly move toward one of other sub-swarms based on the so-far-best cluster number. In the second stage, the particle swarm algorithm is used to cluster the data items. After that, a clustering validity index is applied to evaluate the clustering result of each sub-swarm. The above procedure is repeated until the clustering computation converges. To test the proposed algorithm and compare it with other dynamic clustering algorithms, thirteen test problems, including artificial data sets and UCI data sets, are used. The experimental results show that PSODC has outstanding performance in dynamic data clustering.