基于多克隆的进化免疫网络聚类算法

Clustering method with evolutionary immune network based on polyclonal algorithm

针对传统的聚类算法存在对初始值敏感、易陷入局部最小值,且对类别数和聚类原型的先验知识依赖比较大等问题。提出了一种基于多克隆的进化免疫网络聚类算法,该算法使用了多克隆算子,增加了种群的多样性,扩大了解空间的搜索范围。利用禁忌克隆运算,使处于模糊边界的抗体处于抑制状态,提高了聚类的精度。仿真实验表明,当对具有数值和类属的混合特征属性的数据及具有模糊边界的数据进行聚类时,收敛速度快且不依赖初始原型的选择。

The traditional algorithms of cluster are very sensitive to the initialization and easy to get trapped into local optima, Moreover these algorithms depend more on prior knowledge about the cluster number and the type of clustering prototypes.For this purpose,a clustering method with evolutionary immune network based on polyclonal algorithm is presented,this algorithm is developed by improving clonal selection algorithm based on immune network.The algorithm uses polyclonal operator,just because of this operation,the diversity of antibody group is broadened and the search scope of solution space is widened.The algorithm uses forbidden clone operation so that the antibodies at the fuzzy boundary is in the state of suppression,this operation improves the accuracy of the cluster.The experiment shows that the algorithm has fast convergence and is robust to initialization while clustering for the data sets which have not only mixed numerical and categorical values but also fuzzy boundary values.