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Mautz, Dominik; Ye, Wei; Plant, Claudia; Boehm, Christian (2020): Non-Redundant Subspace Clusterings with Nr-Kmeans and Nr-DipMeans. In: ACM Transactions on Knowledge Discovery From Data, Vol. 14, No. 5, 55
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Abstract

A huge object collection in high-dimensional space can often be clustered in more than one way, for instance, objects could be clustered by their shape or alternatively by their color. Each grouping represents a different view of the dataset. The new research field of non-redundant clustering addresses this class of problems. In this article, we follow the approach that different, non-redundant k-means-like clusterings may exist in different, arbitrarily oriented subspaces of the high-dimensional space. We assume that these subspaces (and optionally a further noise space without any cluster structure) are orthogonal to each other. This assumption enables a particularly rigorous mathematical treatment of the non-redundant clustering problem and thus a particularly efficient algorithm, which we call Nr-Kmeans (for non-redundant k-means). The superiority of our algorithm is demonstrated both theoretically, as well as in extensive experiments. Further, we propose an extension of Nr-Kmeans that harnesses Hartigan's dip test to identify the number of clusters for each subspace automatically.