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Volumn 27, Issue 16, 2011, Pages 2322-2323

CAMPAIGN: An open-source library of GPU-accelerated data clustering algorithms

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; CLUSTER ANALYSIS; COMPUTER GRAPHICS; COMPUTER PROGRAM; DNA MICROARRAY; HUMAN;

EID: 79961194949     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btr386     Document Type: Article
Times cited : (19)

References (12)
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  • 2
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    • Clustering methods for microarray gene expression data
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  • 3
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    • Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics
    • Chodera,J.D. et al. (2007) Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics. J. Chem. Phys., 126, 155101.
    • (2007) J. Chem. Phys. , vol.126 , pp. 155101
    • Chodera, J.D.1
  • 4
    • 17444397705 scopus 로고    scopus 로고
    • Performance guarantees for hierarchical clustering
    • Dasgupta,S. and Long,P.M. (2005) Performance guarantees for hierarchical clustering. J. Comput. Syst. Sci., 70, 555-569.
    • (2005) J. Comput. Syst. Sci. , vol.70 , pp. 555-569
    • Dasgupta, S.1    Long, P.M.2
  • 5
    • 13844276694 scopus 로고    scopus 로고
    • Cluster analysis for gene expression data: a survey
    • Daxin,J. et al. (2004) Cluster analysis for gene expression data: a survey. IEEE Trans. Knowl. Data Eng., 16, 1370-1386.
    • (2004) IEEE Trans. Knowl. Data Eng. , vol.16 , pp. 1370-1386
    • Daxin, J.1
  • 6
    • 78649334862 scopus 로고    scopus 로고
    • Independent component analysis: mining microarray data for fundamental human gene expression modules
    • Engreitz,J.M., et al. (2010) Independent component analysis: mining microarray data for fundamental human gene expression modules. J. Biomed. Inform., 43, 932-944.
    • (2010) J. Biomed. Inform. , vol.43 , pp. 932-944
    • Engreitz, J.M.1
  • 7
    • 0021938963 scopus 로고
    • Clustering to minimize the maximum intercluster distance
    • Gonzalez,T.F. (1985) Clustering to minimize the maximum intercluster distance. Theor. Comput. Sci., 38, 293-306.
    • (1985) Theor. Comput. Sci. , vol.38 , pp. 293-306
    • Gonzalez, T.F.1
  • 9
    • 0020068152 scopus 로고
    • Self-organized formation of topologically correct feature maps
    • Kohonen,T. (1982) Self-organized formation of topologically correct feature maps. Biol. Cybern., 43, 59-69.
    • (1982) Biol. Cybern. , vol.43 , pp. 59-69
    • Kohonen, T.1
  • 10
    • 0020102027 scopus 로고
    • Least squares quantization in PCM
    • Lloyd,S.P. (1982) Least squares quantization in PCM. IEEE Trans. Inf. Theory, IT-28, 129-137.
    • (1982) IEEE Trans. Inf. Theory , vol.IT28 , pp. 129-137
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  • 11
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    • Efficient and effective clustering methods for spatial data mining
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    • Ng,R.T. and Han,J. (1994) Efficient and effective clustering methods for spatial data mining. In J.B. Bocca et al. (eds) VLDB'94, Proceedings of 20th International Conference on Very Large Data Bases, September 12-15, 1994, Santiago de Chile, Chile. Morgan Kaufmann, pp. 144-155.
    • (1994) Proceedings of 20th International Conference on Very Large Data Bases , pp. 144-155
    • Ng, R.T.1    Han, J.2
  • 12
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    • STRALCP-structure alignment-based clustering of proteins
    • Zemla,A. et al. (2007) STRALCP-structure alignment-based clustering of proteins. Nucleic Acids Res., 35, e150.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.