Automatic Clustering Using Differential Evolution with Cluster Number Vibration Strategy

• Main Authors: Shen-Wei Chen, 陳慎微
• Other Authors: Wei-Ping Lee
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/74480922896069532500

碩士 === 中原大學 === 資訊管理研究所 === 98 === In this paper, an improved differential evolution algorithm (V-ACDE) with cluster number vibration strategy for automatic crisp/fuzzy clustering has been presented. The proposed algorithm needs no prior knowledge of the number of clusters of the data. Rather, it finds the optimal number of clusters on the processing with stable and fast convergence, cluster number vibration mechanism will search more possible cluster number in case of bad initial cluster number caused bad clusters. Superiority of the proposed algorithm is demonstrated by comparing it with one recently developed partitional clustering algorithm. Experimental results over four real life datasets and two artificial datasets, and the performance of proposed algorithm is mostly better than the other one.