Density conscious subspace clustering for high-dimensional data

IEEE Transactions on Knowledge and Data Engineering

Volumn 22, Issue 1, 2010, Pages 16-30

  Chu, Yi Hong ^a   Huang, Jen Wei ^b   Chuang, Kun Ta ^a   Yang, De Nian ^c   Chen, Ming Syan ^a,c  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^b YUAN ZE UNIVERSITY   (Taiwan)

^c ACADEMIA SINICA   (Taiwan)

Author keywords

And association rules; Classification; Clustering; Data clustering; Data mining; Subspace clustering.

Indexed keywords

A-DENSITY; CARDINALITIES; CLUSTERING; CLUSTERING ACCURACY; CLUSTERING DATA; CRITICAL PROBLEMS; DATA SETS; DENSE REGION; DENSITY THRESHOLD; DENSITY-BASED APPROACHES; DIVERGENCE PROBLEMS; DIVIDE AND CONQUER; FEATURE SPACE; HIGH DIMENSIONAL DATA; HIGH QUALITY; HIGH-DENSITY REGIONS; INNOVATIVE ALGORITHMS; NOVEL CLUSTERING; SUBSPACE CLUSTERING;

ASSOCIATION RULES; ASSOCIATIVE PROCESSING; CLASSIFICATION (OF INFORMATION); CLUSTER ANALYSIS;

CLUSTERING ALGORITHMS;

EID: 72949091449     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2008.224     Document Type: Article

References (28)

1. C.C. Aggarwal, A. Hinneburg, and D. Keim, "On the Surprising Behavior of Distance Metrics in High Dimensional Space," Proc. Eighth Int'l Conf. Database Theory (ICDT), 2001.
2. C.C. Aggarwal and C. Procopiuc, "Fast Algorithms for Projected Clustering," Proc. ACM SIGMOD Int'l Conf. Management of Data, 1999.
3. C.C. Aggarwal and P.S. Yu, "The IGrid Index: Reversing the Dimensionality Curse for Similarity Indexing in High Dimensional Space," Proc. Sixth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, 2000.
4. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, "Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications," Proc. ACM SIGMOD Int'l Conf. Management of Data, 1998.
5. I. Assent, R. Krieger, E. Muller, and T. Seidl, "DUSC: Dimensionality Unbiased Subspace Clustering," Proc. IEEE Int'l Conf. Data Mining (ICDM), 2007.
6. K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft, "When is Nearest Neighbors Meaningful?" Proc. Seventh Int'l Conf. Database Theory (ICDT), 1999.
7. A. Blum and P. Langley, "Selection of Relevant Features and Examples in Machine Learning," Artificial Intelligence, vol.97, pp. 245-271, 1997.
8. M.-S. Chen, J. Han, and P.S. Yu, "Data Mining: An Overview from Database Perspective," IEEE Trans. Knowledge and Data Eng., vol.8, no.6, pp. 866-883, Dec. 1996.
9. C.H. Cheng, A.W. Fu, and Y. Zhang, "Entropy-Based Subspace Clustering for Mining Numerical Data," Proc. Fifth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, 1999.
10. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, "A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise," Proc. Second Int'l Conf. Knowledge Discovery and Data Mining (SIGKDD), 1996.
11. H. Fang, C. Zhai, L. Liu, and J. Yang, "Subspace Clustering for Microarray Data Analysis: Multiple Criteria and Significance," Proc. IEEE Computational Systems Bioinformatics Conf., 2004.
12. U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurasamy, Advances in Knowledge Discovery and Data Mining. MIT Press, 1996.
13. J. Han and M. Kamber, Data Mining: Concepts and Techniques. Morgan Kaufmann, 2000.
14. J. Han, J. Pei, and Y. Yin, "Mining Frequent Patterns without Candidate Generation," Proc. 2000 ACM SIGMOD Int'l Conf. Management of Data, 2000.
15. S. Hettich and S. Bay, "The UCI KDD Archive," http:// kdd.ics.uci.edu, 1999.
16. A. Hinneburg, C.C. Aggarwal, and D. Keim, "What is the Nearest Neighbor in High Dimensional Spaces?" Proc. 26th Int'l Conf. Very Large Data Bases (VLDB), 2000.
17. K. Kailing, H.-P. Kriegel, and P. Kroger, "Density-Connected Subspace Clustering for High-Dimensional Data," Proc. Fourth SIAM Int'l Conf. Data Mining (SDM), 2004.
18. Y.B. Kim, J.H. Oh, and J. Gao, "Emerging Pattern Based Subspace Clustering of Microarray Gene Expression Data Using Mixture Models," Proc. Int'l Conf. Bioinformatics and Its Applications (ICBA), 2004.
19. H.-P. Kriegel, P. Kroger, M. Renz, and S. Wurst, "A Generic Framework for Efficient Subspace Clustering of High-Dimensional Data," Proc. Fifth IEEE Int'l Conf. Data Mining (ICDM), 2005.
20. H. Liu and H. Motoda, Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998.
21. L. Lu and R. Vidal, "Combined Central and Subspace clustering for Computer Vision Applications," Proc. 23rd Int'l Conf. Machine Learning (ICML), 2006.
22. G. Moise, J. Sander, and M. Ester, "P3C: A Robust Projected Clustering Algorithm," Proc. Sixth IEEE Int'l Conf. Data Mining (ICDM), 2006.
23. H.S. Nagesh, S. Goil, and A. Choudhary, "Adaptive Grids for Clustering Massive Data Sets," Proc. First SIAM Int'l Conf. Data Mining (SDM), 2001.
24. L. Parsons, E. Haque, and H. Liu, "Subspace Clustering for High Dimensional Data: A Review," ACM SIGKDD Explorations Newsletter, vol.6, pp. 90-105, 2004.
25. C.M. Procopiuc, M. Jones, P.K. Agarwal, and T.M. Murali, "A Monte Carlo Algorithm for Fast Projective Clustering," Proc. 2002 ACM SIGMOD Int'l Conf. Management of Data, 2002.
26. UCI Repository of Machine Learning Databases, http://mlaern. ics.uci.edu/MLRepository.html, 1998.
27. K.-G. Woo, J.-H. Lee, M.-H. Kim, and Y.-J. Lee, "FINDIT: A Fast and Intelligent Subspace Clustering Algorithm Using Dimension Voting," Information and Software Technology, vol.46, pp. 255-271, 2004.
28. M.L. Yiu and N. Mamoulis, "Frequent-Pattern Based Iterative Projected Clustering," Proc. Third IEEE Int'l Conf. Data Mining (ICDM), 2003.