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A CLUE for CLUster Ensembles

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  • Kurt Hornik
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    Abstract

    Cluster ensembles are collections of individual solutions to a given clustering problem which are useful or necessary to consider in a wide range of applications. The R package clue provides an extensible computational environment for creating and analyzing cluster ensembles, with basic data structures for representing partitions and hierarchies, and facilities for computing on these, including methods for measuring proximity and obtaining consensus and "secondary" clusterings.

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    File URL: http://www.jstatsoft.org/v14/i12/paper
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    Bibliographic Info

    Article provided by American Statistical Association in its journal Journal of Statistical Software.

    Volume (Year): 14 ()
    Issue (Month): i12 ()
    Pages:

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    Handle: RePEc:jss:jstsof:14:i12

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    Web page: http://www.jstatsoft.org/

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    References

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    1. Leo Katz & James Powell, 1953. "A proposed index of the conformity of one sociometric measurement to another," Psychometrika, Springer, vol. 18(3), pages 249-256, September.
    2. William Day, 1986. "Foreword: Comparison and consensus of classifications," Journal of Classification, Springer, vol. 3(2), pages 183-185, September.
    3. A. Gordon & M. Vichi, 2001. "Fuzzy partition models for fitting a set of partitions," Psychometrika, Springer, vol. 66(2), pages 229-247, June.
    4. Anja Struyf & Mia Hubert & Peter Rousseeuw, . "Clustering in an Object-Oriented Environment," Journal of Statistical Software, American Statistical Association, vol. 1(i04).
    5. Samuel E. Buttrey, . "Calling the lp_solve Linear Program Software from R, S-PLUS and Excel," Journal of Statistical Software, American Statistical Association, vol. 14(i04).
    6. Chris Fraley & Adrian E. Raftery, 2003. "Enhanced Model-Based Clustering, Density Estimation, and Discriminant Analysis Software: MCLUST," Journal of Classification, Springer, vol. 20(2), pages 263-286, September.
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    Cited by:
    1. Fionn Murtagh, 2009. "The Remarkable Simplicity of Very High Dimensional Data: Application of Model-Based Clustering," Journal of Classification, Springer, vol. 26(3), pages 249-277, December.
    2. Wu, Han-Ming, 2011. "On biological validity indices for soft clustering algorithms for gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1969-1979, May.
    3. Yasemin Boztug & Thomas Reutterer, 2006. "A Combined Approach for Segment-Specific Analysis of Market Basket Data," SFB 649 Discussion Papers SFB649DP2006-006, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Guy Brock & Vasyl Pihur & Susmita Datta & Somnath Datta, . "clValid: An R Package for Cluster Validation," Journal of Statistical Software, American Statistical Association, vol. 25(i04).

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