Machine Learning

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

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

    The emergence of high-dimensional data in various areas has brought new challenges to the ensemble clustering research. To deal with the curse of dimensionality, considerable efforts in ensemble clustering have been made by incorporating various subspace-based techniques. 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 number of diversified metrics, and furthermore, how to jointly exploit the multi-level diversity in the large number of metrics, subspaces, and clusters, in a unified ensemble clustering 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. Further, an entropy-based criterion is adopted to explore the cluster-wise diversity in ensembles, based on which the consensus function is therefore presented. Experimental results on twenty high-dimensional datasets have confirmed the superiority of our approach over the state-of-the-art.

    From Subspaces to Metrics and Beyond: Toward Multi-Diversified Ensemble Clustering of High-Dimensional Data
    by Dong Huang, Chang-Dong Wang, Jian-Huang Lai, Chee-Keong Kwoh
    https://arxiv.org/pdf/1710.03113v1.pdf

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