Evolutionary Clustering using Frequent Itemsets

Abstract

Evolutionary clustering is an emerging research area addressing the problem of clustering dynamic data. An evolutionary clustering should take care of two conflicting criteria: preserving the current cluster quality and not deviating too much from the recent history. In this paper we propose an algorithm for evolutionary clustering using frequent itemsets. A frequent itemset based approach for evolutionary clustering is natural and it automatically satisfies the two criteria of evolutionary clustering. We provide theoretical as well as experimental proofs to support our claims. We performed experiments on our approach using different datasets and the results show that our approach is comparable to most of the existing algorithms for evolutionary clustering.