Abstract
Consensus clustering aims to combine multiple base clusters into a probably better and more robust clustering result. Despite the significant progress in recent years, the existing consensus clustering approaches are mostly designed for general-purpose scenarios, yet often lack the ability to effectively and efficiently deal with high-dimensional data. To this end, this paper proposes a subspace-weighted consensus clustering approach, which is based on two key observations in high-dimensional data. First, the cluster structures often lie in different subspaces in high-dimensional feature space. Second, the features in high-dimensional data may be of different importance and should be treated differently. Specifically, we utilize the Laplacian score to estimate the importance of different features. Then the weighted random sampling is performed repeatedly to produce a set of diverse random subspaces, in which multiple base clusters can thereby be generated. Further, the reliability of each base clustering is evaluated and weighted by considering the reliability of the features in the corresponding subspace, after which a subspace-weighted bipartite graph can be constructed and efficiently partitioned to obtain the final consensus clustering result. Experimental results on ten real-world high-dimensional datasets demonstrate the effectiveness and efficiency of the proposed approach.
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This work was supported by NSFC (61976097).
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Cai, X., Huang, D. (2020). Subspace-Weighted Consensus Clustering for High-Dimensional Data. In: Yang, X., Wang, CD., Islam, M.S., Zhang, Z. (eds) Advanced Data Mining and Applications. ADMA 2020. Lecture Notes in Computer Science(), vol 12447. Springer, Cham. https://doi.org/10.1007/978-3-030-65390-3_1
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