基于克隆算法的网络结构聚类新算法

基于目标函数的聚类算法是目前应用最为广泛的聚类分析方法之一.然而这类算法都需要类别数和聚类原型的先验知识,且只能分析具有相同原型的数值型数据.此外这类算法还存在对初始化敏感,易陷入局部极值点等弱点.为此,本文提出一种基于克隆算法的网络结构聚类新算法以实现聚类分析的自动化.由于新算法将克隆选择与禁忌克隆相结合,使网络既具有免疫的特异性又具有免疫的耐受性,通过分析网络神经元的最小生成树,能够快速准确地获得类别数以及相关的分类信息.对各种类型的数据集的测试结果均表明,本文提出的新算法对于处理具有混和特征的数据集聚类分析问题是相当便捷有效的.

A Novel Clustering Method with Network Structure Based on Clonal Algorithm

In the field of cluster analysis,objective function based clustering algorithm is one of widely applied methods so far.However,this type algorithms need the prior knowledge about the cluster number and the type of clustering prototypes,and can only process data sets with the same prototypes.Moreover,these algorithms are very sensitive to the initialization and easy to get trapped into local optima.To this end,this paper presents a novel clustering method with network structure based on clonal algorithm to realize the automation of cluster analysis.Since the new algorithm combines the clonal selection algorithm and forbidden clonal operation,the obtained network has not only the specificity but also the tolerance of immunity.By analyzing the neurons of obtained network with minimal spanning tree,one can easily get the cluster number and related classification information.The test results with various data sets illustrate that the novel algorithm achieves more effective performances on cluster analyzing the data set with mixed numeric values and categorical values.