人工免疫C-均值聚类算法

通过借鉴生物免疫系统中的克隆选择原理和记忆机制，提出了一种人工免疫C-均值混合聚类算法．该算法采用了新的克隆选择方法，通过亲和度排序和个体浓度定义了个体的选择概率，从而可确定个体的适应值评价函数，以评价和选择个体．算法还集成了一种C-均值搜索算子，用于加快收敛速度．在聚类数目已知的情况下，所提算法能够得到给定数据集下的全局最优划分，与基于遗传算法的聚类方法比较，它具有更快的收敛速度和更高的收敛精度，并可扩展到性能指标能够表示为优化聚类中心函数的聚类模型之中．仿真结果表明，所提算法是有效性的．

Artificial Immune C-Means Clustering Algorithm

Inspired by the clone selection principle and memory mechanism of the vertebrate immune system, a hybrid algorithm combining C-means algorithm and artificial immune algorithm is presented. A new clone selection strategy is used and the individual selection probability is defined through sorting the affinity and individuals concentration so that the evaluating function of the individual fitness can be determined,and then individuals are evaluated and selected. The C-means algorithm is treated as a new search operator in order to improve the convergence speed. Comparing with the genetic algorithm based clustering approaches the proposed algorithm can converge to the global optimum faster and has higher accuracy. Given the cluster number, the algorithm can obtain the best partition of data sets. The algorithm can be extended to other clustering model whose objective function can be represented in terms of optimization of cluster centers. Experimental results indicate the validity of the proposed algorithm.