A shrinking-based approach for multi-dimensional data analysis

Proceedings - 29th International Conference on Very Large Data Bases, VLDB 2003

Volumn , Issue , 2003, Pages 440-451

  Shi, Yong ^a   Song, Yuqing ^a   Zhang, Aidong ^a  

^a State University of New York at Buffalo ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION ANALYSIS; PATTERN RECOGNITION; SIGNAL PROCESSING;

CLUSTER EVALUATIONS; CONNECTED COMPONENT; DATA ANALYSIS TECHNIQUES; DATA PREPROCESSING TECHNIQUE; DATA REORGANIZATION; HIGH DIMENSIONAL SPACES; MULTI-DIMENSIONAL DATA ANALYSIS; MULTIDIMENSIONAL DATA;

CLUSTERING ALGORITHMS;

EID: 85012120070     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1016/b978-012722442-8/50046-x     Document Type: Conference Paper

References (27)

1. C. Aggarwal, C. Procopiuc, J. Wolf, P. Yu, and J. Park. Fast algorithms for projected clustering. In Proceedings of the ACM SIGMOD CONFERENCE on Management of Data, pages 61-72, Philadelphia, PA, 1999.
2. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In Proceedings of the ACM SIGMOD Conference on Management of Data, pages 94-105, Seattle, WA, 1998.
3. N. Ahuja. Dot pattern processing using voronoi neighborhoods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 4(3):336-343, May 1982.
4. Ankerst M., Breunig M. M., Kriegel H.-P., Sander J. OPTICS: Ordering Points To Identify the Clustering Structure. Proc. ACM SIG-MOD Int. Conf. on Management of Data (SIGMOD'99), Philadelphia, PA, pages 49-60, 1999.
5. S. D. Bay. The UCI KDD Archive [http://kdd.ics.uci.edu]. University of California, Irvine, Department of Information and Computer Science.
6. Chi-Farn Chen, Jyh-Ming Lee. The Validity Measurement of Fuzzy C-Means Classifier for Remotely Sensed Images. In Proc. ACRS 2001-22nd Asian Conference on Remote Sensing, 2001.
7. T. H. Cormen, C. E. Leiserson, and R. L. Rivest. Introduction to Algorithms. The MIT Press, 1990.
8. D. Barbara, W. DuMouchel, C. Faloutsos, P. J. Haas, J. H. Hellerstein, Y. Ioannidis, H. V. Jagadish, T. Johnson, R. Ng, V. Poosala, K. A. Ross, and K. C. Servcik. The New Jersey data resuction report. Bulletin of the Technical Committee on Data Engineering, 1997.
9. M. Ester, K. H.-P., J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, 1996.
10. S. Guha, R. Rastogi, and K. Shim. Cure: An efficient clustering algorithm for large databases. In Proceedings of the ACM SIG-MOD conference on Management of Data, pages 73-84, Seattle, WA, 1998.
11. S. Guha, R. Rastogi, and K. Shim. Rock: A robust clustering algorithm for categorical attributes. In Proceedings of the IEEE Conference on Data Engineering, 1999.
12. A. Hinneburg and D. A. Keim. An efficient approach to clustering in large multimedia databases with noise. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, pages 58-65, New York, August 1998.
13. J. MacQueen. Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Volume I, Statistics., 1967.
14. A. Jain, M. Murty, and P. Flyn. Data clustering: A review. ACM Computing Surveys, 31(3), 1999.
15. L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley & Sons, 1990.
16. L. J. Cowen, C. E. Priebe. Randomized non-linear projections uncover high-dimensional structure. pages 319-331, 1997.
17. Maria Halkidi, Michalis Vazirgiannis. A Data Set Oriented Approach for Clustering Algorithm Selection. In PKDD, 2001.
18. Maria Halkidi, Michalis Vazirgiannis. Clustering Validity Assessment: Finding the Optimal Partitioning of a Data Set. In ICDM, 2001.
19. Maria Halkidi, Yannis Batistakis, Michalis Vazirgiannis. Clustering Algorithms and Validity Measures. In SSDBM, 2001.
20. R. T. Ng and J. Han. Efficient and Effective Clustering Methods for Spatial Data Mining. In Proceedings of the 20th VLDB Conference, pages 144-155, Santiago, Chile, 1994.
21. T. Redman. Data Quality: Management and Technology. Bantam Books, 1992.
22. Rothman, Milton A. The laws of physics. New York, Basic Books, 1963.
23. T. Seidl and H. Kriegel. Optimal multi-step k-nearest neighbor search. In Proceedings of the ACM SIGMOD conference on Management of Data, pages 154-164, Seattle, WA, 1998.
24. G. Sheikholeslami, S. Chatterjee, and A. Zhang. Wavecluster: A multi-resolution clustering approach for very large spatial databases. In Proceedings of the 24th International Conference on Very Large Data Bases, 1998.
25. W. Wang, J. Yang, and R. Muntz. STING: A Statistical Information Grid Approach to Spatial Data Mining. In Proceedings of the 23rd VLDB Conference, pages 186-195, Athens, Greece, 1997.
26. Warren Viessman Jr., Gary L. Lewis. Introduction to Hydrology, 4/e. Prentice Hall, 1996.
27. T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH: An Efficient Data Clustering Method for Very Large Databases. In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, pages 103-114, Montreal, Canada, 1996.