Graph-based clustering with constraints

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

Volumn 6635 LNAI, Issue PART 2, 2011, Pages 51-62

  Anand, Rajul ^a   Reddy, Chandan K ^a  

^a Wayne State University   (United States)

Author keywords

Clustering; constrained clustering; graph based clustering

Indexed keywords

CLUSTER ANALYSIS; DATA MINING; GRAPH ALGORITHMS; GRAPHIC METHODS;

BACK-GROUND KNOWLEDGE; CLUSTERING; CLUSTERING PROCESS; CONSTRAINED CLUSTERING; DISTANCE FUNCTIONS; DISTANCE MEASURE; GRAPH PARTITIONING; GRAPH-BASED CLUSTERING;

CLUSTERING ALGORITHMS;

EID: 79957934375     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-20847-8_5     Document Type: Conference Paper

References (19)

1. Basu, S., Banerjee, A.,Mooney, R.J.: Semi-supervised clustering by seeding. In: Proceedings of the Nineteenth International Conference on Machine Learning (ICML 2002), pp. 27-34 (2002)
2. Basu, S., Bilenko, M., Mooney, R.J.: A probabilistic framework for semi-supervised clustering. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 59-68 (2004)
3. Basu, S., Davidson, I., Wagstaff, K.: Constrained Clustering: Advances in Algorithms, Theory, and Applications. Chapman & Hall/CRC (2008)
4. Bilenko, M., Basu, S., Mooney, R.J.: Integrating constraints and metric learning in semi-supervised clustering. In: Proceedings of the Twenty-first International Conference on Machine Learning, ICML 2004 (2004)
5. Davidson, I., Ravi, S.S.: Agglomerative Hierarchical Clustering with Constraints: Theoretical and Empirical Results. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 59-70. Springer, Heidelberg (2005)
6. Davidson, I., Ravi, S.S., Shamis, L.: A sat-based framework for efficient constrained clustering. In: Jonker, W., Petković, M. (eds.) SDM 2010. LNCS, vol. 6358, pp. 94-105. Springer, Heidelberg (2010)
7. Davidson, I.,Wagstaff, K.L., Basu, S.: Measuring Constraint-Set Utility for Partitional Clustering Algorithms. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 115-126. Springer, Heidelberg (2006)
8. Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
9. Gunopulos, D., Vazirgiannis, M., Halkidi, M.: From unsupervised to semi-supervised learning: Algorithms and evaluation approaches. In: SIAMInternational Conference on Data Mining: Tutorial (2006)
10. Halkidi, M., Gunopulos, D., Kumar, N., Vazirgiannis, M., Domeniconi, C.: A framework for semi-supervised learning based on subjective and objective clustering criteria. In: Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), pp. 637-640 (2005)
11. Karypis, G., Han, E.-H., Kumar, V.: Chameleon: Hierarchical clustering using dynamic modeling. IEEE Computer 32(8), 68-75 (1999)
12. Karypis, G., Kumar, V.: Metis 4.0: Unstructured graph partitioning and sparse matrix ordering system. Tech. Report, Dept. of Computer Science, Univ. of Minnesota (1998)
13. Klein, D., Kamvar, S.D.,Manning, C.D.: From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering. In: Proceedings of the Nineteenth International Conference on Machine Learning (ICML 2002), pp. 307-314 (2002)
14. Kulis, B., Basu, S., Dhillon, I.S., Mooney, R.J.: Semi-supervised graph clustering: a kernel approach. In: Proceedings of the Twenty-Second International Conference on Machine Learning (ICML 2005), pp. 457-464 (2005)
15. Lelis, L., Sander, J.: Semi-supervised density-based clustering. In: Perner, P. (ed.) ICDM 2009. LNCS, vol. 5633, pp. 842-847. Springer, Heidelberg (2009)
16. Rand,W.M.: Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 66(336), 846-850 (1971)
17. Ruiz, C., Spiliopoulou, M., Menasalvas, E.: Density based semi-supervised clustering. Data Mining and Knowledge Discovery 21(3), 345-370 (2009)
18. Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining, US edition. Addison Wesley, Reading (2005)
19. Wagstaff, K., Cardie, C., Rogers, S., Schrödl, S.: Constrained k-means clustering with background knowledge. In: Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), pp. 577-584 (2001)