Knowledge based Cluster Ensemble

Abstract

Although there exist a lot of cluster ensemble approaches, few of them consider the prior knowledge of the datasets. In this paper, we propose a new cluster ensemble approach called knowledge based cluster ensemble (KCE) which incorporates the prior knowledge of the dataset into the cluster ensemble framework. Specifically, the prior knowledge of the dataset is first represented by the side information which is encoded as pairwise constraints. Then, KCE generates a set of cluster solutions by the basic clustering algorithm. Next, KCE transforms the pairwise constraints to the confidence factor of the cluster solutions. In the following, the new data matrix is constructed by considering all the cluster solutions and their corresponding confidence factor. Finally, the results are obtained by partitioning the consensus matrix. The experiments illustrate that (1) KCE works well on the real datasets; (2) KCE outperforms most of the state-of-art cluster ensemble approaches.