CARE: Finding local linear correlations in high dimensional data

Proceedings - International Conference on Data Engineering

Volumn , Issue , 2008, Pages 130-139

  Zhang, Xiang ^a   Pan, Feng ^a   Wang, Wei ^a  

^a Brooks Computer Science Building   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATIONS; CORRELATION METHODS; FEATURE EXTRACTION; HEURISTIC ALGORITHMS; HEURISTIC PROGRAMMING; TECHNOLOGY;

BIO-MEDICAL APPLICATIONS; CORRELATION MEASUREMENTS; DATA ENGINEERING; DATA POINTS; FEATURE PROJECTION; FEATURE SELECTION; FEATURE SPACES; FEATURE SUBSETS; FEATURE TRANSFORMATIONS; GLOBAL PATTERNS; GLOBAL TRANSFORMATION; HIGH-DIMENSIONAL DATA; INTERNATIONAL CONFERENCES; LOCAL LINEAR; MONOTONICITY; NUMEROUS APPLICATIONS; PATTERN STRUCTURES; PROJECTED CLUSTERING; SEARCH SPACES; SPECTRUM PROPERTIES;

CLUSTERING ALGORITHMS;

EID: 52649097914     PISSN: 10844627     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDE.2008.4497421     Document Type: Conference Paper

References (22)

1. M. Eisen, P. Spellman, P. Brown, and D. Botstein, "Cluster analysis and display of genome-wide expression patterns," Proc. Natl. Acad. Sci. USA, vol. 95, pp. 14 863-68, 1998.
2. V. Iyer and et. al., "The transcriptional program in the response of human fibroblasts to serum," Science, vol. 283, pp. 83-87, 1999.
3. L. Parsons, E. Haque, and H. Liu, "Subspae clustering for high dimensional data: a review," KDD Explorations, 2004.
4. A. Blum and P. Langley, "Selection of relevant features and examples in machine learning," Artificial Intelligence, vol. 97, pp. 245-271, 1997.
5. H. Liu and H. Motoda, Feature selection for knowledge discovery and data mining. Boston: Kluwer Academic Publishers, 1998.
6. L. Yu and H. Liu, "Feature selection for high-dimensional data: a fast correlation-based filter solution." Proceedings of International Conference on Machine Learning, 2003.
7. Z. Zhao and H. Liu, "Searching for interacting features," the 20th International Joint Conference on AI, 2007.
8. I. Jolliffe, Principal component analysis. New York: Springer, 1986.
9. T. Hastie, R. Tibshirani, and J. Friedman, The elements of statistical learning. Springer Verlag, 1996.
10. C. Aggarwal and P. Yu, "Finding generalized projected clusters in high dimensional spaces," in SIGMOD, 2000.
11. C. Bohm, K. Kailing, P. Kroger, and A. Zimek, "Computing clusters of correlation connected objects," in SIGMOD, 2004.
12. E. Achtert, C. Bohm, H.-P. Kriegel, P. Kroger, and A. Zimek, "Deriving quantitative models for correlation clusters," in KDD, 2006.
13. H. Wang, W. Wang, J. Yang, and Y. Yu, "Clustering by pattern similarity in large data sets," SIGMOD, 2002.
14. M. Ashburner and et al., "Gene ontology: tool for the unification of biology," The gene ontology consortium, Nat. Genet., vol. 25, pp. 25-29, 2000.
15. H. R. Lindman, Analysis of variance in complex experimental designs, Wiley-Interscience, 2001.
16. K. Fukunaga, Introduction to statistical pattern recognition. Academic Press, San Diego, California, 1990.
17. W. Mendenhall and T. Sincich. A Second Course in Statistics: Regression Analysis. Prentice Hall, 2002.
18. S. Yu, K. Yu, H.-P. Kriegel, and M. Wu, "Supervised probabilistic principal component analysis," KDD, 2006.
19. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. "Automatic sub-space clustering of high dimensinal data for data mining applications," SIGMOD, 1998.
20. C. Aggarwal, J. Wolf, P. Yu, C. Procopiuc, and J. Park, "Fast algorithms for projected clustering," SIGMOD, 1999.
21. C. Chen, A. Fu, and Y. Zhang, "Entropy-based subspace clustering for mining numerical data," SIGKDD, 1999.
22. R. A. Horn and C. R. Johnson, Matrix analysis. Cambridge U. K.: Cambridge University Press, 1985.