A statistics-based approach to control the quality of subclusters in incremental gravitational clustering

Pattern Recognition

Volumn 38, Issue 12, 2005, Pages 2256-2269

  Chen, Chien Yu ^a,b   Hwang, Shien Ching ^a,c   Oyang, Yen Jen ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^b YUAN ZE UNIVERSITY   (Taiwan)

^c ASIA UNIVERSITY   (Taiwan)

Author keywords

Data clustering; Gravity theory; Hierarchical clustering; Incremental learning

Indexed keywords

DATA CLUSTERING; GRAVITY THEORY; HIERARCHICAL CLUSTERING; INCREMENTAL LEARNING;

ALGORITHMS; DATA REDUCTION; DATABASE SYSTEMS; HIERARCHICAL SYSTEMS; STATISTICAL PROCESS CONTROL;

PATTERN RECOGNITION;

EID: 25144476363     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2005.03.005     Document Type: Article

References (25)

1. R.J. Brachman, T. Anand, in: U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy (Eds.), The Process of Knowledge Discovery in Databases, Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, Cambridge, MA, 1996, pp. 37-57.
2. B. Everitt Cluster Analysis 1980 Halsted Press New York
3. A.K. Jain, and R.C. Dubes Algorithms for clustering data 1988 Prentice-Hall Englewood Cliffs, NJ
4. A.K. Jain, M.N. Murty, and P.J. Flynn Data clustering: a review ACM Comput. Surv. 31 3 1999 264 323
5. M.N. Murty, and G. Krishina A computationally efficient technique for data clustering Pattern Recognition 12 1980 153 158
6. J. Han, and M. Kamber Data Mining: Concepts and Techniques 2000 Morgan Kaufmann Publishers San Francisco
7. J. Han, M. Kamber, and A.K.H. Tung Spatial clustering methods in data mining: a survey H. Miller J. Han Geographic Data Mining and Knowledge Discovery 2001 Taylor and Francis London
8. I.H. Witten Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations 2000 Morgan Kaufmann San Francisco, CA
9. J.A. Hartigan Clustering Algorithms 1975 Wiley New York
10. D. Fisher, Improving inference through conceptual clustering, Proceedings of the sixth National Conference on Artificial Intelligence (AAAI-87), 1987, pp. 461-465.
11. J. Gennari, P. Langley, and D. Fisher Models of incremental concept formation Artif. Intell. 40 1989 11 61
12. M. Charikar, C. Chekuri, T. Feder, R. Motwani, Incremental clustering and dynamic information retrieval, Proceedings of the 29th Annual ACM Symposium on Theory of Computing (STOC-97), 1997, pp. 626-634.
13. M. Ester, H.-P. Kriegel, J. Sander, M. Wimmer, X. Xu, Incremental clustering for mining in a data warehousing environment, Proceedings of the 24th International Conference on Very Large Data Bases (VLDB-98), 1998, pp. 323-333.
14. V. Ganti, J. Gehrke, and R. Ramakrishnan DEMON: mining and monitoring evolving data IEEE Trans. Knowledge Data Eng. 13 1 2001 50 63
15. A. Ribert, A. Ennaji, Y. Lecourtier, An incremental hierarchical clustering, Proceedings of the 1999 Vision Interface Conference, 1999, pp. 586-591.
16. T. Zhang, R. Ramakrishnan, M. Livny, BIRCH: an efficient data clustering method for very large databases, Proceedings of 1996 ACM-SIGMOD International Conference on Management of Data (SOGMOD-96), 1996, pp. 103-114.
17. L. Talavera, J. Bejar, Using multistrategy learning to scale up hierarchical clustering algorithms, Proceedings of the Second International Workshop on Extraction of Knowledge from Data Bases (EKDB-99), 1999.
18. W.E. Wright Gravitational clustering Pattern Recognition 9 1977 151 166
19. S. Kundu Gravitational clustering - a new approach based on the spatial distribution of the points Pattern Recognition 32 1999 1149 1160
20. Y.-J. Oyang, C.-Y. Chen, T.-W. Yang, A study on the hierarchical data clustering algorithm based on gravity theory, Proceedings of the Fifth European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD-01), 2001, pp. 350-361.
21. Y.-J. Oyang, C.-Y. Chen, S.-C. Hwang, C.-F. Lin, Characteristics of a Hierarchical Data Clustering Algorithm Based on Gravity Theory, Technical Report of NTUCSIE 02-01, 2001 (available at http://mars.csie.ntu.edu.tw/~cychen/publications_on_dm.htm ).
22. T.V. Ravi, and C.K. Gowda Clustering of symbolic objects using gravitational approach IEEE Trans. Syst. Man Cybern. - Part B: Cybernetics 29 6 1999 888 894
23. R.V. Hogg, and E.A. Tanis Probability and Statistical Inference 2001 Prentice-Hall Englewood Cliffs, NJ
24. M. Stonebraker, J. Frew, K. Gardels, J. Meredith, The Sequoia 2000 storage benchmark, Proceedings of the 1993 ACM-SIGMOD International Conference on Management of Data (SIGMOD-93), 1993, pp. 2-11.
25. C.L. Blake, C.J. Merz, UCI repository of machine learning databases, Technical report, University of California, Department of Information and Computer Science, Irvine, CA, 1998.