Visual Quality Assessment of Subspace Clusterings

Abstract

The quality assessment of results of clustering algorithms is challenging as different cluster methodologies lead to different cluster characteristics and topologies. A further complication is that in high-dimensional data, subspace clustering adds to the complexity by detecting clusters in multiple different lower-dimensional projections. The quality assessment for (subspace) clustering is especially difficult if no benchmark data is available to compare the clustering results. In this research paper, we present SubEval, a novel subspace evaluation framework, which provides visual support for comparing quality criteria of subspace clusterings. We identify important aspects for evaluation of subspace clustering results and show how our system helps to derive quality assessments. SubEval allows assessing subspace cluster quality at three different granularity levels: (1) A global overview of similarity of clusters and estimated redundancy in cluster members and subspace dimensions. (2) A view of a selection of multiple clusters supports in-depth analysis of object distributions and potential cluster overlap. (3) The detail analysis of characteristics of individual clusters helps to understand the (non-)validity of a cluster. We demonstrate the usefulness of SubEval in two case studies focusing on the targeted algorithm- and domain scientists and show how the generated insights lead to a justified selection of an appropriate clustering algorithm and an improved parameter setting. Likewise, SubEval can be used for the understanding and improvement of newly developed subspace clustering algorithms. SubEval is part of SubVA, a novel open-source web-based framework for the visual analysis of different subspace analysis techniques.