基于人工免疫网络的动态聚类算法

聚类分析的两个基本任务是分析数据集中簇的数量以及这些簇的位置.大多数的聚类方法通常只关注后一个问题.为了在聚类数不确定的情况下实现聚类分析,本文提出了一种新的结合人工免疫网络和遗传算法的动态聚类算法—DCBIG.新算法主要包含两个阶段:先使用人工免疫网络算法获得聚类可行解,然后使用遗传算法依据聚类可行解实现动态聚类.本文对获得聚类可行解的条件和概率进行了分析.仿真实验结果表明与现有方法相比,新方法具有更高的收敛概率和收敛速度.

A Novel Dynamic Clustering Algorithm Based on Artificial Immune Network

Cluster analysis aims at answering two main questions:how many clusters there are in the data set and where they are located.Usually,the traditional clustering algorithms only focus on the last problem.In order to solve the two problems at the same time,this paper proposes a novel dynamic clustering algorithm called DCBIG,which is based on the immune network and genetic algorithm.The algorithm includes two phases,begins by running immune network algorithm to find a feasible solution,and then employs genetic algorithm to search the optimum number of clusters and the location of each cluster according to the feasible solution.Also,the probabilities and the conditions to acquire a feasible solution through immune network algorithm are discussed in this paper.Experimental results show that new algorithm is characterized by higher convergent probability and convergent speed.