Constraint-based clustering in large databases

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1973, Issue , 2001, Pages 405-419

  Tung, Anthony K H ^a   Han, Jiawei ^a   Lakshmanan, Laks V S ^b   Ng, Raymond T ^c  

^a SIMON FRASER UNIVERSITY   (Canada)

^b INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

^c UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS;

CONSTRAINED CLUSTERING; CONSTRAINT-BASED CLUSTERING; INITIAL SOLUTION; LARGE DATABASE; OBJECT MOVEMENTS; OPTIMAL SOLUTIONS; TRADITIONAL CLUSTERING; USER-SPECIFIED CONSTRAINTS;

K-MEANS CLUSTERING;

EID: 84949423737     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44503-x_26     Document Type: Conference Paper

References (17)

1. [AGGR98] R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In SIGMOD'98.
2. [BBD00] P. Bradley, K. P. Bennet, and A. Demiriz. Constrained k-means clustering. In MSR-TR-2000-65, Microsoft Research, May 2000.
3. [BFR98] P. Bradley, U. Fayyad, and C. Reina. Scaling clustering algorithms to large databases. In KDD'98.
4. [EKSX96] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases. In KDD'96.
5. [GJ79] M. Garey and D. Johnson. Computers and Intractability: a Guide to The Theory of NP-Completeness. Freeman and Company, New York, 1979.
6. [HaKa00] J. Han and M. Kamber Data Mining: Concepts and Techniques. Morgan Kaufmann, 2000.
7. [KHK99] G. Karypis, E.-H. Han, and V. Kumar. CHAMELEON: A hierarchical clustering algorithm using dynamic modeling. COMPUTER, 32:68-75, 1999.
8. [KMR97] D. Karger, R. Motwani, and G. D. S. Ramkumar. On approximating the longest path in a graph. Algorithmica, 18:99-110, 1997.
9. [KPR98] J. M. Kleinberg, C. Papadimitriou, and P. Raghavan. A microeconomic view of data mining. Data Mining and Knowledge Discovery, 2:311-324, 1998.
10. [KR90] L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley and Sons, 1990.
11. [LNHP99] L. V. S. Lakshmanan, R. Ng, J. Han, and A. Pang. Optimization of con-strained frequent set queries with 2-variable constraints. In SIGMOD'99.
12. [NH94] R. Ng and J. Han. Effcient and effective clustering method for spatial data mining. In VLDB'94.
13. [NLHP98] R. Ng, L. V. S. Lakshmanan, J. Han, and A. Pang. Exploratory mining and pruning optimizations of constrained associations rules. In SIGMOD'98.
14. [STA97] D. B. Shmoys, E. Tardos, and K. Aardal. Approximation algorithms for facility location problems. In STOC'97.
15. [TNLH00] A. K. H. Tung, R. Ng, L. Lakshmanan, and J. Han. Constraint-based clustering in large databases. www.cs.sfu.ca/pub/cs/techreports/2000/CMPT2000-05.pdf.
16. [WYM97] W. Wang, J. Yang, and R. Muntz. STING: A statistical information grid approach to spatial data mining. In VLDB'97.
17. [ZRL96] T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH: an effcient data clustering method for very large databases. In SIGMOD'96.