Constraint-based graph clustering through node sequencing and partitioning

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

Volumn 3056, Issue , 2004, Pages 41-51

  Qian, Yu ^a   Zhang, Kang ^a   Lai, Wei ^b  

^a The University of Texas at Dallas   (United States)

^b SWINBURNE UNIVERSITY OF TECHNOLOGY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

GRAPH THEORY; COMPUTER PROGRAMMING; CONSTRAINT THEORY; GRAPHIC METHODS; MATHEMATICAL MODELS;

CONSTRAINT-BASED; GRAPH CLUSTERING; GRAPH PARTITIONING; MIN-MAX; MIN-MAX PRINCIPLE; OBJECTIVE FUNCTIONS; PRACTICAL REQUIREMENTS; TRADITIONAL APPROACHES;

DATA MINING; ARTIFICIAL INTELLIGENCE;

CONSTRAINT SOLVING; DATA SETS; SHANNON INFORMATION; SUBSPACE CLUSTERING;

EID: 7444223091     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-24775-3_7     Document Type: Conference Paper

References (12)

1. Bradley, P. S., Bennett, K. P., and Demiriz, A. Constrained K-Means Clustering, In MSRTR- 2000-65, Microsoft Research. (2000)
2. Cheng, C-K., and Wei, Y. A. An improved two-way partitioning algorithm with stable performance. IEEE. Trans. on Computed Aided Design. 10 (1991), pp. 1502-1511.
3. Ding, H. Q. C., He, X., Zha, H., Gu, M., and Simon, H. A Min-Max Cut Algorithm for Graph Partitioning and Data Clustering. Proc. of International Conf on Data Mining, (2001), pp. 107-114.
4. Donath, W. E. and Hoffman, A. J. Lower bounds for partitioning of graphs. IBM J. Res. Develop., 17 (1973), pp. 420-425.
5. Fisher, D. Knowledge acquisition via incremental conceptual clustering, Machine Learning, 2, (1987), pp. 139-172.
6. Hagen, L. and Kahng, A. B. New spectral methods for ratio cut partitioning and clustering. IEEE Trans. on Computed Aided Design, 11(1992), pp. 1074-1085.
7. Kamada, T. and Kawai, S. An algorithm for drawing general undirected graphs. Information Processing Letters, 31(1989), pp. 7-15.
8. Kandemir, M., Banerjee, P., Ramanujam, J., and Shenoy, N. A global communication optimization technique based on data-flow analysis and linear algebra. ACM Transactions on Programming Languages and Systems, Vol. 21, No. 6, (2000) pp.1251-1297.
9. Qian, Y. and Zhang, K.: A Customizable Hybrid Approach to Data Clustering. Proc. of the 2003 ACM Symposium on Applied Computing, (2003) 485-489.
10. Shi, J. and Malik, J.: Normalized cuts and image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence. Vol. 22, No. 8, (2000), pp. 888-905.
11. Tung, A. K. H., Han, J., Lakshmanan, L. V. S., and Ng, R. T. Constrained-based clustering in large databases, Proc. 8th Intl. Conf. on Database Theory (ICDT’01), London, UK, (2001), pp. 405-419.
12. Wagstaff, K. and Cardie, C. Clustering with instance-level constraints, Proc. of the 17th Intl. Conf. on Machine Learning, (2000), pp. 1103-1110.