Chen Yao-wen, Huang Lin, Luo Wei-ming, Huang Jing-xia, Wu Ren-hua
Shantou University Medical College, Shantou 515041, China.
Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:1048-51. doi: 10.1109/IEMBS.2008.4649339.
According to the basis of clonal selection immune algorithm and hierarchical clustering, a dynamic clonal selection immune clustering algorithm is presented, which no pre-knowledge is needed. The proposed algorithm bases on antibody affinity, to recognize antigen, restrain and merge antibody. By using aiNET immune network model, the algorithm mutates location of antibodies, in which the mutating rate is dynamically adjusted with inverse proportion to the number of immune evolution generations. After dynamic mutation, the similar antibodies are merged again, and the same processes repeats until it meets the ending condition. Experimental results showed that the proposed algorithm is more coincidental reality of clustering and more preferable performance than traditional ones.
基于克隆选择免疫算法和层次聚类，提出了一种动态克隆选择免疫聚类算法，该算法无需先验知识。所提算法基于抗体亲和力来识别抗原、抑制和合并抗体。通过使用aiNET免疫网络模型，算法对抗体位置进行变异，其中变异率与免疫进化代数成反比动态调整。动态变异后，再次合并相似抗体，并重复相同过程直至满足结束条件。实验结果表明，所提算法比传统算法更符合聚类实际情况且性能更优。
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