【摘要】 模糊C均值聚类 (FCM)是一种广泛采用的动态聚类方法 ,其聚类效果往往受初始聚类中心的影响。受自适应免疫系统对入侵机体的抗原产生免疫记忆的机理启示 ,提出了一种新的产生初始聚类中心的方法。算法中 ,待分析的数据被视为入侵性抗原 ,产生的记忆细胞作为聚类分析的初始中心。克隆选择用来产生抗原的记忆细胞群体 ,免疫网络理论则用来抑制该群体规模的快速增长。实验结果表明免疫记忆机理用于FCM初始中心的选择是可行的 ,不仅提高了FCM算法的收敛速度 ,而且可以通过改变阈值的大小自动决定类别数更多还原

【Abstract】 The fuzzy C-means algorithm (FCM) is widely used for dynamic clustering. The performance of FCM depends on the selection of the initial cluster center. Inspired by the mechanism that the adaptive immune system remembers the antigen exposed to the body before, a novel algorithm is proposed for the generation of the initial cluster center. In this algorithm, the data set to be analyzed is taken as the invading antigen and the memory cell generated acts as the initial cluster center. While the clonal selection principle is responsible for generating the memory cell population, the immune network theory prevents the population size from increasing quickly. The experimental results have shown the feasibility of applying the immunological memory mechanism to the selection of initial centers in dynamic clustering. By adopting this algorithm, not only the accuracy and the convergence speed of FCM are improved, but also the number of clusters does not require to be predefined; it depends on the threshold to be chosen.更多还原