2008 FindingNonRedundantStatisticall

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Projected and subspace clustering algorithms search for clusters of points in subsets of attributes. Projected clustering computes several disjoint clusters, plus outliers, so that each cluster exists in its own subset of attributes. Subspace clustering enumerates clusters of points in all subsets of attributes, typically producing many overlapping clusters. One problem of existing approaches is that their objectives are stated in a way that is not independent of the particular algorithm proposed to detect such clusters. A second problem is the definition of cluster density based on user-defined parameters, which makes it hard to assess whether the reported clusters are an artifact of the algorithm or whether they actually stand out in the data in a statistical sense.

We propose a novel problem formulation that aims at extracting axis-parallel regions that stand out in the data in a statistical sense. The set of axis-parallel, statistically significant regions that exist in a given data set is typically highly redundant. Therefore, we formulate the problem of representing this set through a reduced, non-redundant set of axis-parallel, statistically significant regions as an optimization problem. Exhaustive search is not a viable solution due to computational infeasibility, and we propose the approximation algorithm STATPC. Our comprehensive experimental evaluation shows that STATPC significantly outperforms existing projected and subspace clustering algorithms in terms of accuracy.

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2008 FindingNonRedundantStatisticallGabriela Moise
Jörg Sander
Finding Non-redundant, Statistically Significant Regions in High Dimensional Data: A Novel Approach to Projected and Subspace ClusteringKDD-2008 Proceedings10.1145/1401890.14019562008
AuthorGabriela Moise + and Jörg Sander +
doi10.1145/1401890.1401956 +
journalProceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining +
titleFinding Non-redundant, Statistically Significant Regions in High Dimensional Data: A Novel Approach to Projected and Subspace Clustering +
year2008 +