Semi-Supervised Clustering Ensemble Based on Cluster Consensus Selection

Abstract

Ensemble clustering emerged as an important extension of classical clustering problems and is one of the most recent advances in unsupervised learning. Its purpose is to combine the results obtained using different algorithms by a consensus function so that the final solution is more favorable than the individual clustering algorithms. In this study, we propose a semi-supervised clustering ensemble framework using cluster consensus selection, which tries to improve the accuracy of clustering results. In general, there are two types of semi-supervised clustering algorithms, including constraint-based and metric-based. Here, the proposed ensemble clustering algorithm is equipped with a semi-supervised clustering mechanism based on pairwise constraints. Since the complexity of consensus functions scales with the number of clustering methods, processing big data for ensemble clustering is sometimes slow or impossible. Usually, all primary clusters from all clustering methods are used in the consensus function. However, the merit of clusters from different methods can be considered to improve the consensus quality. Accordingly, we propose a cluster consensus selection approach that selects a subset of meriting primary clusters to participate in the final consensus. Here, Normalized Mutual Information (NMI) is developed to measure the merit of clusters. Meanwhile, reducing the number of primary clusters in the consensus function can enable big data clustering. The proposed algorithm is very computationally efficient and provides linear complexity in clustering. Experimental results show the effectiveness of the proposed algorithm in terms of different performance metrics such as NMI, ARI and CPCC.