HIERARCHICAL DATA CLUSTERING USING AINET IMMUNE NETWORK
The clustering problem can be viewed as an optimization problem that locates the optimal centroids of the clusters directly. This view permits the use of evolutionary algorithm to do data clustering tasks. Recently, as a new branch in evolutionary computing, artificial immune systems (ALS)have shown great powerful in knowledge acquisition,pattern recognition, classification, and optimization tasks.The aiNet, one such AlS algorithm exploiting the biologically inspired features of the immune system,performs well on small dimension data clustering tasks. This paper proposes the use of the aiNet to more complex tasks of data clustering. Based on the immune network and affinity maturation principles, the aiNet condensed the raw data and extracted knowledge from the data set. Combined with hierarchical clustering and graph techniques, the immune network showed good performance in data clustering tasks.
hierarchical data clustering artificial immune network aiNet
Li Liu
School of Information Technology, Southern Yangtze University Wuxi,Jiangsu,214000, China
国际会议
杭州
英文
644-647
2006-10-12（万方平台首次上网日期，不代表论文的发表时间）