Robust one-class clustering Using hybrid global and local search

ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning

Volumn , Issue , 2005, Pages 273-280

  Gupta, Gunjan ^a   Ghosh, Joydeep ^a  

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

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA REDUCTION; LEARNING SYSTEMS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; SET THEORY;

LOCAL SEARCH; ONE-CLASS INFORMATION BALL (OC-IB); PRUNING OUTLIERS; ROBUST ONE-CLASS CLUSTERING;

ROBUSTNESS (CONTROL SYSTEMS);

EID: 31844451945     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1102351.1102386     Document Type: Conference Paper

References (14)

1. Ash A. Alizadeh et al. (2000). Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling. Nature, 403, 503-511.
2. Banerjee, A., Merugu, S., Dhillon, I., & Ghosh, J. (2004). Clustering with Bregman divergences. Proc. SDM2004 (pp. 234-245).
3. Crammer, K., & Chechik, G. (2004). A needle in a haystack: Local one-class optimization. ICML. Banff, Alberta, Canada.
4. Dhillon, I. S., Marcotte, E. M., & Roshan, U. (2003). Diametrical clustering for identifying anti-correlated gene clusters. Bioinformatics, 19, 1612-1619.
5. Dhillon, I. S., & Modha, D. S. (2001). Concept decompositions for large sparse text data using clustering. Machine Learning, 42, 143-175.
6. Gasch A. P. et al. (2000). Genomic expression programs in the response of yeast cells to environmental changes. Molecular Biology of the Cell, 11, 4241-4257.
7. Mansson, R., Tsapogas, P., Akerlund, M., & et. al. (2004). Pearson correlation analysis of microarray data allows for the identification of genetic targets for early b-cell factor. J. Biol. Chem., 279, 17905-17913.
8. Pietra, S. D., Pietra, V. D., & Lafferty, J. (2001). Duality and auxiliary functions for bregman distances. Technical Report CMU-CS-01-109, School of Computer Science. Carnegie Mellon University.
9. Rockafeller, R. T. (1970). Convex analysis. Princeton University Press.
10. Schölkopf, B., Burges, C., & Vapnik, V. (1995). Extracting support data for a given task. KDD. Menlo Park, CA: AAAI Press.
11. Schölkopf, B., Platt, J. C., Shawe-Taylor, J. S., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13, 1443-1471.
12. Sharan, R., & Shamir, R. (2000). Click: A clustering algorithm with applications to gene expression analysis. Proc. 8th ISMB, 307-316.
13. Tax, D., & Duin, R. (1999). Data domain description using support vectors. Proceedings of the European Symposium on Artificial Neural Networks (pp. 251-256).
14. White, D., & Jain, R. (1996). Similarity indexing with ss-tree. 12th International Conf. on Data Engineering (pp. 516-523). New Orleans.