Design of K-Means Clustering Algorithm Based on Distance Concentration

摘要：

Using the immune recognizing principle, the data object to cluster was denoted as the antigens set, and the clustering center was the antibodies set. The clustering was the process to obtain the best antibodies to catch the antigens by producing the antibodies and recognizing the antigens unceasingly. The distance concentration and the affinity, between antibody and antigen, and between antibody and antibody, were defined about the K-means clustering; the antibody reproduction function was proposed. The antibody cloning algorithm was presented. The experimental results show that the algorithm not only avoids the local optima and is robust to initialization, but also increases the convergence speed.

作者: Tao Liu Guiping Dai Li Zhang Zhijie Wang

作者单位: Suzhou Vocational University 1158 Yuehu road, Suzhou, P.R.China Shanghai DianJi University 690 Jiangchuan road, Shanghai, P.R.China

会议类型: 国际会议

会议名称: Second International Symposium on Electronic Commerce and Security(第二届电子商务与安全国际研究大会)(ISECS 2009)

会议地点: 南昌

会议语种:英文

页码: 912-915

在线出版日期: 2009-05-22（万方平台首次上网日期，不代表论文的发表时间）