A Novel Dynamic Clustering Algorithm Based on Tabu Search
基于Tabu搜索的聚类算法研究

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 DCBIT, which is based on the immune network and Tabu search. The algorithm includes two phases, it begins by running im- mune network algorithm to find a candidate clustering center set, and then it employs Tabu search to search the opti- mum number of clusters and the location of each cluster according to the candidate centers. Experimental results show that the hew algorithm has satisfied convergent probability and convergent speed.