Non-Redundant Subspace Clusterings with Nr-Kmeans and Nr-DipMeans

Mautz, Dominik; Ye, Wei; Plant, Claudia und Boehm, Christian (2020): Non-Redundant Subspace Clusterings with Nr-Kmeans and Nr-DipMeans. In: ACM Transactions on Knowledge Discovery From Data, Bd. 14, Nr. 5, 55

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.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Mathematik, Informatik und Statistik > Informatik
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• ISSN: 1556-4681
• Sprache: Englisch