Evaluating Clustering in Subspace Projections of High Dimensional Data

Proceedings of the VLDB Endowment

Volumn 2, Issue 1, 2009, Pages 1270-1281

  Müller, Emmanuel ^a   Günnemann, Stephan ^a   Assent, Ira ^b   Seidl, Thomas ^a  

^a RWTH AACHEN UNIVERSITY   (Germany)

^b AALBORG UNIVERSITY   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING RESULTS; EVALUATION MEASURES; EXPERIMENTAL ANALYSIS; HIGH DIMENSIONAL DATA; PROJECTED CLUSTERING; SUB-SPACE CLUSTERING; SUBSPACE PROJECTION; SYNTHETIC DATASETS;

CLUSTERING ALGORITHMS;

EID: 84865086248     PISSN: None     EISSN: 21508097     Source Type: Conference Proceeding    
DOI: 10.14778/1687627.1687770     Document Type: Article

References (32)

1. C. Aggarwal, J. Wolf, P. Yu, C. Procopiuc, and J. Park. Fast algorithms for projected clustering. In SIGMOD, pages 61-72, 1999.
2. C. Aggarwal and P. Yu. Finding generalized projected clusters in high dimensional spaces. In SIGMOD, pages 70-81, 2000.
3. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In SIGMOD, pages 94-105, 1998.
4. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In VLDB, pages 487-499, 1994.
5. I. Assent, R. Krieger, E. Müller, and T. Seidl. DUSC: Dimensionality unbiased subspace clustering. In ICDM, pages 409-414, 2007.
6. I. Assent, R. Krieger, E. Müller, and T. Seidl. INSCY: Indexing subspace clusters with in-process-removal of redundancy. In ICDM, pages 719-724, 2008.
7. I. Assent, E. Müller, R. Krieger, T. Jansen, and T. Seidl. Pleiades: Subspace clustering and evaluation. In ECML PKDD, pages 666-671, 2008.
8. A. Asuncion and D. Newman. UCI Machine Learning Repository, 2007.
9. K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft. When is nearest neighbors meaningful. In IDBT, pages 217-235, 1999.
10. B. Bringmann and A. Zimmermann. The chosen few: On identifying valuable patterns. In ICDM, pages 63-72, 2007.
11. Y. Cheng and G. M. Church. Biclustering of expression data. In International Conference on Intelligent Systems for Molecular Biology, pages 93-103, 2000.
12. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, 39(1):1-38, 1977.
13. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases. In KDD, pages 226-231, 1996.
14. J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2001.
15. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In SIGMOD, pages 1-12, 2000.
16. I. Joliffe. Principal Component Analysis. Springer, New York, 1986.
17. K. Kailing, H.-P. Kriegel, and P. Kröger. Density-connected subspace clustering for high-dimensional data. In SDM, pages 246-257, 2004.
18. E. Keogh, L. Wei, X. Xi, S.-H. Lee, and M. Vlachos. LB Keogh supports exact indexing of shapes under rotation invariance with arbitrary representations and distance measures. In VLDB, pages 882-893, 2006.
19. H.-P. Kriegel, P. Kröger, M. Renz, and S. Wurst. A generic framework for effcient subspace clustering of high-dimensional data. In ICDM, pages 250-257, 2005.
20. J. MacQueen. Some methods for classi cation and analysis of multivariate observations. In Berkeley Symp. Math. stat. & prob., pages 281-297, 1967.
21. G. Moise and J. Sander. Finding non-redundant, statistically significant regions in high dimensional data: a novel approach to projected and subspace clustering. In KDD, pages 533-541, 2008.
22. G. Moise, J. Sander, and M. Ester. P3C: A robust projected clustering algorithm. In ICDM, pages 414-425, 2006.
23. E. Müller, I. Assent, S. Güunnemann, T. Jansen, and T. Seidl. OpenSubspace: An open source framework for evaluation and exploration of subspace clustering algorithms in WEKA. In Open Source in Data Mining Workshop at PAKDD, pages 2-13, 2009.
24. E. Müller, I. Assent, R. Krieger, S. Günnemann, and T. Seidl. DensEst: Density estimation for data mining in high dimensional spaces. In SDM, pages 173-184, 2009.
25. E. Müller, I. Assent, R. Krieger, T. Jansen, and T. Seidl. Morpheus: Interactive exploration of subspace clustering. In KDD, pages 1089-1092, 2008.
26. E. Müller, I. Assent, and T. Seidl. HSM: Heterogeneous subspace mining in high dimensional data. In SSDBM, pages 497-516, 2009.
27. H. Nagesh, S. Goil, and A. Choudhary. Adaptive grids for clustering massive data sets. In SDM, 2001.
28. A. Patrikainen and M. Meila. Comparing subspace clusterings. TKDE, 18(7):902-916, 2006.
29. C. e. a. Procopiuc. A monte carlo algorithm for fast projective clustering. In SIGMOD, pages 418-427, 2002.
30. K. Sequeira and M. Zaki. SCHISM: A new approach for interesting subspace mining. In ICDM, pages 186-193, 2004.
31. I. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, USA, 2005.
32. M. L. Yiu and N. Mamoulis. Frequent-pattern based iterative projected clustering. In ICDM, pages 689-692, 2003.