Toward Multi-Diversified Ensemble Clustering of High-Dimensional Data: From Subspaces to Metrics and Beyond

The rapid emergence of high-dimensional data in various areas has brought new challenges to current ensemble clustering research. To deal with the curse of dimensionality, recently considerable efforts in ensemble clustering have been made by incorporating different subspace-based techniques. However, besides the emphasis on subspaces, rather limited attention has been paid to the potential diversity in similarity/dissimilarity metrics. It remains a surprisingly open problem in ensemble clustering how to create and aggregate a large population of diversified metrics, and furthermore, how to jointly investigate the multi-level diversity in the large populations of metrics, subspaces, and clusters in a unified framework. To tackle this problem, this paper proposes a novel multi-diversified ensemble clustering approach. In particular, we create a large number of diversified metrics by randomizing a scaled exponential similarity kernel, which are then coupled with random subspaces to form a large set of metric-subspace pairs. Based on the similarity matrices derived from these metric-subspace pairs, an ensemble of diversified base clusterings can thereby be constructed. Thereafter, an entropy-based criterion is adopted to explore the cluster-wise diversity in ensembles. By jointly exploiting the multi-level diversity in metrics, subspaces, and clusters, three specific ensemble clustering algorithms are finally presented. Experimental results on 30 real-world high-dimensional datasets (including 18 cancer gene expression datasets and 12 image/speech datasets) have demonstrated the superiority of the proposed algorithms over the state-of-the-art.