On the effects of constraints in semi-supervised hierarchical clustering

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

Volumn 4087 LNAI, Issue , 2006, Pages 57-66

  Kestler, Hans A ^a,b   Kraus, Johann M ^a,b   Palm, Günther ^a   Schwenker, Friedhelm ^a  

^a UNIVERSITY OF ULM   (Germany)

^b UNIVERSITY HOSPITAL ULM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CONSTRAINT THEORY; DATABASE SYSTEMS; HIERARCHICAL SYSTEMS;

CLUSTERING ALGORITHM; DENDROGRAMS; HIERARCHIAL CLUSTERING;

DATA STRUCTURES;

EID: 33749412340     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11829898_6     Document Type: Conference Paper

References (15)

1. Demiriz, A., Bennett, K., Embrechts, M.: Semi-supervised clustering using genetic algorithms. In: Artificial Neural Networks in Engineering, New York, Troy (1999) 809-814
2. Wagstaff, K., Cardie, C.: Clustering with instance-level constraints. In: Proceedings of the 17th International Conference on Machine Learning, San Francisco, CA, USA, Morgan Kaufmann Publishers Inc. (2000) 1103-1110
3. Basu, S., Banerjee, A., Mooney, R.: Semi-supervised clustering by seeding. In: Proceedings of 19th International Conference on Machine Learning. (2002) 19-26
4. Klein, D., Kamvar, S., C.Manning: From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering. In: Proceedings of 19th International Conference on Machine Learning. (2002) 307-314
5. Bilenko, M., Mooney, R.: Adaptive duplicate detection using learnable string similarity measures. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. (2003) 39-48
6. Cohn, D., Caruana, R., McCallum, A.: Semi-supervised clustering with user feedback. Technical report, Cornell University (2003)
7. Xing, E., Ng, A., Jordan, M., S.Russell: Distance metric learning, with application to clustering with side-information. In: Advances in Neural Information Processing Systems 15. (2003) 505-512
8. Bar-Hillel, A., Hertz, T., Shental, N., Weinshall, D.: Learning distance functions using equivalence relations. In: Proceedings of 20th International Conference on Machine Learning. (2003) 11-18
9. Davidson, I., Ravi, S.: Agglomerative hierarchical clustering with constraints: Theoretical and empirical results. In: Knowledge Discovery in Databases: 9th European Conference on Principles and Practice of Knowledge Discovery in Databases. Volume 3721 of Lecture Notes in Computer Science., Springer (2005) 59-70
10. Wagstaff, K., Cardie, C., Rogers, S., Schroedl, S.: Constrained k-means clustering with background knowledge. In: Proceedings of 18th International Conference on Machine Learning. (2001) 577-584
11. Bilenko, M., S.Basu, Mooney, R.: Integrating constraints and metric learning in semi-supervised clustering. In: Proceedings of the 21st International Conference on Machine Learning. (2004)
12. Buchholz, M., Kestler, H., Bauer, A., Böck, W., Rau, B., Leder, G., Kratzer, W., Bommer, M., Scarpa, A., Schilling, M., Adler, G., Hoheisel, J., Gress, T.: Specialized dna arrays for the differentiation of pancreatic tumors: A solution for a common diagnostic dilemma. Clin Cancer Res 11 (2005) 8048-8054
13. A.K.Jain, Dubes, R.: Algorithms for Clustering Data. Prentice Hall, New Jersey (1988)
14. Rand, W.: Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 66 (1971) 846-850
15. Eisen, M., Spellman, P., Brown, P., Botstein, D.: Cluster analysis and display of genome-wide expression patterns genetics cluster analysis and display of genomewide expression patterns. PNAS 95 (1998) 14863-14868