Subspace clustering for complex data

Abstract

Clustering is an established data mining technique for grouping objects based on their mutual similarity. Since in today's applications, however, usually many characteristics for each object are recorded, one cannot expect to find similar objects by considering all attributes together. In contrast, valuable clusters are hidden in subspace projections of the data. As a general solution to this problem, the paradigm of subspace clustering has been introduced, which aims at automatically determining for each group of objects a set of relevant attributes these objects are similar in. In this work, we introduce novel methods for effective subspace clustering on various types of complex data: vector data, imperfect data, and graph data. Our methods tackle major open challenges for clustering in subspace projections. We study the problem of redundancy in subspace clustering results and propose models whose solutions contain only non-redundant and, thus, valuable clusters. Since different subspace projections represent different views on the data, often several groupings of the objects are reasonable. Thus, we propose techniques that are not restricted to a single partitioning of the objects but that enable the detection of multiple clustering solutions.