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Volumn , Issue , 2006, Pages 232-243

Bregman bubble clustering: A robust, scalable framework for locating multiple, dense regions in data

Author keywords

[No Author keywords available]

Indexed keywords

BREGMAN BUBBLE CLUSTERING (BBC); DENSE REGIONS IN DATA; SINGLE DENSE CLUSTER;

EID: 84878084024     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2006.32     Document Type: Conference Paper
Times cited : (15)

References (18)
  • 1
    • 12244295756 scopus 로고    scopus 로고
    • An objective evaluation criterion for clustering
    • Seattle, Washington, USA, August
    • A. Banerjee and J. Langford. An objective evaluation criterion for clustering. In KDD-04, Seattle, Washington, USA, August 2004.
    • (2004) KDD-04
    • Banerjee, A.1    Langford, J.2
  • 2
    • 26244461684 scopus 로고    scopus 로고
    • Clustering with Bregman divergences
    • A. Banerjee, S. Merugu, I. Dhillon, and J. Ghosh. Clustering with Bregman divergences. JMLR, 6:1705-1749, 2005.
    • (2005) JMLR , vol.6 , pp. 1705-1749
    • Banerjee, A.1    Merugu, S.2    Dhillon, I.3    Ghosh, J.4
  • 3
    • 14344258245 scopus 로고    scopus 로고
    • K. Crammer and G. Chechik. A needle in a haystack: Local one-class optimization. In In Proc. ICML, Banff, Alberta, Canada, 2004.
    • K. Crammer and G. Chechik. A needle in a haystack: Local one-class optimization. In In Proc. ICML, Banff, Alberta, Canada, 2004.
  • 4
    • 0034824884 scopus 로고    scopus 로고
    • Concept decompositions for large sparse text data using clustering
    • January-February
    • I. S. Dhillon and D. S. Modha. Concept decompositions for large sparse text data using clustering. Machine Learning, 42(1-2):143-175, January-February 2001.
    • (2001) Machine Learning , vol.42 , Issue.1-2 , pp. 143-175
    • Dhillon, I.S.1    Modha, D.S.2
  • 5
    • 84878031528 scopus 로고    scopus 로고
    • M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In In Proc. KDD-96, 1996.
    • M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In In Proc. KDD-96, 1996.
  • 6
    • 0033637153 scopus 로고    scopus 로고
    • Genomic expression programs in the response of yeast cells to environmental changes
    • December
    • Gasch A. P. et al. Genomic expression programs in the response of yeast cells to environmental changes. Mol. Bio. of the Cell, 11(3):4241-4257, December 2000.
    • (2000) Mol. Bio. of the Cell , vol.11 , Issue.3 , pp. 4241-4257
    • Gasch, A.P.1
  • 7
    • 0037249405 scopus 로고    scopus 로고
    • The Stanford microarray database: Data access and quality assessment tools
    • Gollub J. et al. The Stanford microarray database: data access and quality assessment tools. Nucleic Acids Res, 31:94-96, 2003.
    • (2003) Nucleic Acids Res , vol.31 , pp. 94-96
    • Gollub, J.1
  • 8
    • 31844451945 scopus 로고    scopus 로고
    • Robust one-class clustering using hybrid global and local search
    • Bonn, Germany, August
    • G. Gupta and J. Ghosh. Robust one-class clustering using hybrid global and local search. In Proc. ICML 2005, pages 273-280, Bonn, Germany, August 2005.
    • (2005) Proc. ICML 2005 , pp. 273-280
    • Gupta, G.1    Ghosh, J.2
  • 9
    • 0034568109 scopus 로고    scopus 로고
    • Gene shaving as a method for identifying distinct sets of genes with similar expression patterns
    • Hastie T. et al. Gene shaving as a method for identifying distinct sets of genes with similar expression patterns. Genome Biology, 1:1-21, 2000.
    • (2000) Genome Biology , vol.1 , pp. 1-21
    • Hastie, T.1
  • 11
    • 84942597099 scopus 로고    scopus 로고
    • D. Jiang, J. Pei, and A. Zhang. DHC: A density-based hierarchical clustering method for time series gene expression data. In BIBE '03, page 393, Washington, DC, USA, 2003. IEEE Comp. Soc.
    • D. Jiang, J. Pei, and A. Zhang. DHC: A density-based hierarchical clustering method for time series gene expression data. In BIBE '03, page 393, Washington, DC, USA, 2003. IEEE Comp. Soc.
  • 13
    • 9444239213 scopus 로고    scopus 로고
    • A probabilistic functional network of yeast genes
    • I. Lee, S. V. Date, A. T. Adai, and E. M. Marcotte. A probabilistic functional network of yeast genes. Science, 306:1555-1558, 2004.
    • (2004) Science , vol.306 , pp. 1555-1558
    • Lee, I.1    Date, S.V.2    Adai, A.T.3    Marcotte, E.M.4
  • 14
    • 0002788893 scopus 로고    scopus 로고
    • A view of the EM algorithm that justifies incremental, sparse, and other variants
    • M. I. Jordan, editor, Kluwer
    • R. Neal and G. Hinton. A view of the EM algorithm that justifies incremental, sparse, and other variants. In M. I. Jordan, editor, Learning in Graphical Models. Kluwer, 1998.
    • (1998) Learning in Graphical Models
    • Neal, R.1    Hinton, G.2
  • 15
    • 0004027463 scopus 로고    scopus 로고
    • Duality and auxiliary functions for Bregman distances
    • Technical Report CMU-CS-01-109, School of Computer Science, Carnegie Mellon University
    • S. D. Pietra, V. D. Pietra, and J. Lafferty. Duality and auxiliary functions for Bregman distances. In Technical Report CMU-CS-01-109, School of Computer Science, Carnegie Mellon University, 2001.
    • (2001)
    • Pietra, S.D.1    Pietra, V.D.2    Lafferty, J.3
  • 18
    • 0001986205 scopus 로고    scopus 로고
    • Data domain description using support vectors
    • D. Tax and R. Duin. Data domain description using support vectors. In Proceedings of the ESANN-99, pages 251-256, 1999.
    • (1999) Proceedings of the ESANN-99 , pp. 251-256
    • Tax, D.1    Duin, R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.