A New Ant Colony Algorithm for a General Clustering
Ant colony algorithm (ACA), inspired by the food-searching behavior of ants, is an evolutionary algorithm and performs well in discrete optimization. In this paper, a new kind of general clustering algorithm based on ACA is presented according to the principle that how human do clustering and the action that how ants look for food. With this algorithm, we need not take some time to gain the initial clustering center, so it is a general method. According to the statistics, the subjective fact influencing on appraising results could be avoided. Moreover, we can obtain the interval of clustering radius through local search. Finally, this algorithm has been implemented and tested on a real datasets. The performance of this algorithm is compared with the other popular method, which used by 1. Our computation simulations reveal very encouraging results in terms of clustering ability and the method is an efficient and effective approach.
Jiang Huifeng Chen Senfa
Southeast University; Nanjing, 210096, China
国际会议
2007年IEEE灰色系统与智能服务国际会议(2007 IEEE International Conference on Grey Systems and Intelligent Services)
南京
英文
2007-11-18（万方平台首次上网日期，不代表论文的发表时间）