Non-Redundant Subspace Clusterings with Nr-Kmeans and Nr-DipMeans
Dominik Mautz, Wei Ye, Claudia Plant, Christian Böhm · ACM Transactions on Knowledge Discovery from Data · 2020
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 N r -K means (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 N r -K means that harnesses Hartigan’s dip test to identify the number of clusters for each subspace automatically.