NDLI: Revealing true subspace clusters in high dimensions

Content Provider	IEEE Xplore Digital Library
Author	Jinze Liu Strohmaier, K. Wei Wang
Copyright Year	2004
Description	Author affiliation: Dept. of Comput. Sci., North Carolina Univ., Chapel Hill, NC, USA (Jinze Liu; Strohmaier, K.; Wei Wang)
Abstract	Subspace clustering is one of the best approaches for discovering meaningful clusters in high dimensional space. One cluster in high dimensional space may be transcribed into multiple distinct maximal clusters by projecting onto different subspaces. A direct consequence of clustering independently in each subspace is an overwhelmingly large set of overlapping clusters which may be significantly similar. To reveal the true underlying clusters, we propose a similarity measurement of the overlapping clusters. We adopt the model of Gaussian tailed hyper-rectangles to capture the distribution of any subspace cluster. A set of experiments on a synthetic dataset demonstrates the effectiveness of our approach. Application to real gene expression data also reveals impressive meta-clusters expected by biologists.
Sponsorship	IEEE Comput. Soc. Tech. Comm. on Computational Intelligence (TCCI) IEEE Comput. Soc. Tech. Comm. on Pattern Analysis and Machine Intelligence (TCPAMI)
Starting Page	463
Ending Page	466
File Size	207443
Page Count	4
File Format	PDF
ISBN	0769521428
DOI	10.1109/ICDM.2004.10034
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-11-01
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biological system modeling Clustering algorithms Gene expression Data mining Algorithm design and analysis Merging Computer science Adhesives Tail Entropy Gene Expression Subspace Clustering Overlapping Cluster Adhesion Gaussian Tails Cluster Intersection Local Grid
Content Type	Text
Resource Type	Article

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