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Volumn , Issue , 2009, Pages 241-248

A scalable framework for discovering coherent co-clusters in noisy data

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING PROBLEMS; CO-CLUSTERING; CO-CLUSTERS; DATA POINTS; DATA SETS; FEATURE SPACE; MICROARRAY DATA ANALYSIS; NOISY DATA; REAL-LIFE APPLICATIONS;

EID: 71149116739     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (23)

References (26)
  • 1
    • 34548691246 scopus 로고    scopus 로고
    • A generalized maximum entropy approach to Bregman co-clustering and matrix approximation
    • Banerjee, A., Dhillon, I., Ghosh, J., Merugu, S., & Modha, D. (2007). A generalized maximum entropy approach to Bregman co-clustering and matrix approximation. Jl. Machine Learning Research, 8, 1919-1986.
    • (2007) Jl. Machine Learning Research , vol.8 , pp. 1919-1986
    • Banerjee, A.1    Dhillon, I.2    Ghosh, J.3    Merugu, S.4    Modha, D.5
  • 3
    • 0036375743 scopus 로고    scopus 로고
    • Discovering local structure in gene expression data: The order-preserving submatrix problem
    • Ben-Dor, A., Chor, B., Karp, R., & Yakhini, Z. (2002). Discovering local structure in gene expression data: the order-preserving submatrix problem. Proc. Research in Comp. Mol. Bio. '02 (pp. 49-57).
    • (2002) Proc. Research in Comp. Mol. Bio. '02 , pp. 49-57
    • Ben-Dor, A.1    Chor, B.2    Karp, R.3    Yakhini, Z.4
  • 6
    • 49249096441 scopus 로고    scopus 로고
    • Co-clustering of human cancer microarrays using minimum sum-squared residue co-clustering
    • Cho, H., & Dhillon, I. (2008). Co-clustering of human cancer microarrays using minimum sum-squared residue co-clustering. IEEE/ACM Trans. on Comp. Bio. and Bioinfo., 5, 385-400.
    • (2008) IEEE/ACM Trans. on Comp. Bio. and Bioinfo , pp. 385-400
    • Cho, H.1    Dhillon, I.2
  • 8
    • 2942723846 scopus 로고    scopus 로고
    • A divisive information-theoretic feature clustering algorithm for text classification
    • Dhillon, I., Mallela, S., & Kumar, R. (2003a). A divisive information-theoretic feature clustering algorithm for text classification. Jl. Machine Learning Research, 3, 1265-1287.
    • (2003) Jl. Machine Learning Research , vol.3 , pp. 1265-1287
    • Dhillon, I.1    Mallela, S.2    Kumar, R.3
  • 10
    • 0000550189 scopus 로고    scopus 로고
    • A density-based algorithm for discovering clusters in large spatial databases with noise
    • Ester, M., Kriegel, H., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Proc. Know-Disc. and Data Mining '96.
    • (1996) Proc. Know-Disc. and Data Mining '96
    • Ester, M.1    Kriegel, H.2    Sander, J.3    Xu, X.4
  • 11
    • 0033637153 scopus 로고    scopus 로고
    • Genomic expression program in the response of yeast cells to environmental changes
    • Gasch, A., Spellman, P., Kao, C., Carmel-Harel, et al. (2000). Genomic expression program in the response of yeast cells to environmental changes. Molecular Cell Biology, 11, 4241-4257.
    • (2000) Molecular Cell Biology , vol.11 , pp. 4241-4257
    • Gasch, A.1    Spellman, P.2    Kao, C.3    Harel, C.4
  • 12
    • 0036735386 scopus 로고    scopus 로고
    • Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma
    • Gordon, G. J., Jensen, R. V., Hsiao, L., Gullans, S. R., et al. (2002). Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. Cancer Research, 62, 4963-4967.
    • (2002) Cancer Research , vol.62 , pp. 4963-4967
    • Gordon, G.J.1    Jensen, R.V.2    Hsiao, L.3    Gullans, S.R.4
  • 13
    • 84878084024 scopus 로고    scopus 로고
    • Bregman bubble clustering: A robust, scalable framework for locating multiple, dense regions in data
    • Gupta, G., & Ghosh, J. (2006). Bregman bubble clustering: A robust, scalable framework for locating multiple, dense regions in data. Proc. Int. Conf. on Data Mining '06 (pp. 232-243).
    • (2006) Proc. Int. Conf. on Data Mining '06 , pp. 232-243
    • Gupta, G.1    Ghosh, J.2
  • 14
    • 0036012349 scopus 로고    scopus 로고
    • Plaid models for gene expression data
    • Lazzeroni, L., & Owen, A. B. (2002). Plaid models for gene expression data. Statistica Sinica, 12, 61-86.
    • (2002) Statistica Sinica , vol.12 , pp. 61-86
    • Lazzeroni, L.1    Owen, A.B.2
  • 15
    • 4344602134 scopus 로고    scopus 로고
    • Simultaneous feature selection and clustering using a mixture model
    • Law, M., Figueiredo, M., & A.K. Jain (2004). Simultaneous feature selection and clustering using a mixture model. IEEE Trans. PAMI, 26, 1154-1166.
    • (2004) IEEE Trans. PAMI , vol.26 , pp. 1154-1166
    • Law, M.1    Figueiredo, M.2    Jain, A.K.3
  • 16
    • 9444239213 scopus 로고    scopus 로고
    • A probabilistic functional network of yeast genes
    • Lee, I., Date, S., Adai, A., & Marcotte, E. (2004). A probabilistic functional network of yeast genes. Science, 306, 1555-1558.
    • (2004) Science , vol.306 , pp. 1555-1558
    • Lee, I.1    Date, S.2    Adai, A.3    Marcotte, E.4
  • 18
    • 0041627879 scopus 로고    scopus 로고
    • Extracting conserved gene expression motifs from gene expression data
    • Murali, T., & Kasif, S. (2003). Extracting conserved gene expression motifs from gene expression data. Pacific Symposium on Biocomp., 8, 77-88.
    • (2003) Pacific Symposium on Biocomp , vol.8 , pp. 77-88
    • Murali, T.1    Kasif, S.2
  • 19
    • 17044376078 scopus 로고    scopus 로고
    • Sub-space clustering for high dimensional data: A review
    • Parsons, L., Haque, E., & Liu, H. (2004). Sub-space clustering for high dimensional data: a review. SIGKDD Explor. Newsl., 6, 90-105.
    • (2004) SIGKDD Explor. Newsl , vol.6 , pp. 90-105
    • Parsons, L.1    Haque, E.2    Liu, H.3
  • 20
    • 33646137384 scopus 로고    scopus 로고
    • A systematic comparison and evaluation of biclustering methods for gene expression data
    • Prelic, A., Bleuler, S., Zimmermann, P., Wille, A., & et. al (2006). A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics, 22(9), 1122-1129.
    • (2006) Bioinformatics , vol.22 , Issue.9 , pp. 1122-1129
    • Prelic, A.1    Bleuler, S.2    Zimmermann, P.3    Wille, A.4    & et., al.5
  • 21
    • 11244306358 scopus 로고    scopus 로고
    • Discovering statistically significant biclusters in gene expression data
    • Tanay, A., Sharan, R., & Shamir, R. (2002). Discovering statistically significant biclusters in gene expression data. Bioinformatics, 18, 136-144.
    • (2002) Bioinformatics , vol.18 , pp. 136-144
    • Tanay, A.1    Sharan, R.2    Shamir, R.3
  • 23
    • 84944178665 scopus 로고
    • Hierarchical grouping to optimize an objective function
    • Ward, J. (1963). Hierarchical grouping to optimize an objective function. Jl. of American Stat. Assoc, 58, 236-244.
    • (1963) Jl. of American Stat. Assoc , vol.58 , pp. 236-244
    • Ward, J.1
  • 24
    • 33749620002 scopus 로고    scopus 로고
    • Mining shifting-and-scaling co-regulation patterns on gene expression profiles
    • Xu, X., Lu, Y., Tung, A., & Wang, W. (2006). Mining shifting-and-scaling co-regulation patterns on gene expression profiles. Proc. Int. Conf. on Data Engg. '06 (p. 89).
    • (2006) Proc. Int. Conf. on Data Engg. '06 , pp. 89
    • Xu, X.1    Lu, Y.2    Tung, A.3    Wang, W.4
  • 25
    • 27544510864 scopus 로고    scopus 로고
    • Discovering coherent biclusters from gene expression data using zero-suppressed binary decision diagrams
    • Yoon, S., Nardini, C., Benini, L., & Micheli, G. D. (2005). Discovering coherent biclusters from gene expression data using zero-suppressed binary decision diagrams. IEEE/ACM Trans. on Comp. Bio. and Bioinfo., 2, 339-354.
    • (2005) IEEE/ACM Trans. on Comp. Bio. and Bioinfo , pp. 339-354
    • Yoon, S.1    Nardini, C.2    Benini, L.3    Micheli, G.D.4
  • 26
    • 52649099915 scopus 로고    scopus 로고
    • Mining approximate order preserving clusters in the presence of noise
    • Zhang, M., Wang, W., & Liu, J. (2008). Mining approximate order preserving clusters in the presence of noise. Proc. Int. Conf. on Data Engg. '08 (pp. 160-168).
    • (2008) Proc. Int. Conf. on Data Engg. '08 , pp. 160-168
    • Zhang, M.1    Wang, W.2    Liu, J.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.