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On biological validity indices for soft clustering algorithms for gene expression data

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  • Wu, Han-Ming
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    Abstract

    Unsupervised clustering methods such as K-means, hierarchical clustering and fuzzy c-means have been widely applied to the analysis of gene expression data to identify biologically relevant groups of genes. Recent studies have suggested that the incorporation of biological information into validation methods to assess the quality of clustering results might be useful in facilitating biological and biomedical knowledge discoveries. In this study, we generalize two bio-validity indices, the biological homogeneity index and the biological stability index, to quantify the abilities of soft clustering algorithms such as fuzzy c-means and model-based clustering. The results of an evaluation of several existing soft clustering algorithms using simulated and real data sets indicate that the soft versions of the indices provide both better precision and better accuracy than the classical ones. The significance of the proposed indices is also discussed.

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    File URL: http://www.sciencedirect.com/science/article/B6V8V-51S258Y-2/2/80202b7ad3dc0925440af9302fbde37c
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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 55 (2011)
    Issue (Month): 5 (May)
    Pages: 1969-1979

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    Handle: RePEc:eee:csdana:v:55:y:2011:i:5:p:1969-1979

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    Web page: http://www.elsevier.com/locate/csda

    Related research

    Keywords: Biological validity indices Fuzzy clustering Fuzzy cluster validity Fuzzy c-means Gene expression Microarray data analysis Soft clustering;

    References

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    1. Qiu, Weiliang & Joe, Harry, 2006. "Separation index and partial membership for clustering," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 585-603, February.
    2. Kurt Hornik, . "A CLUE for CLUster Ensembles," Journal of Statistical Software, American Statistical Association, vol. 14(i12).
    3. Fraley C. & Raftery A.E., 2002. "Model-Based Clustering, Discriminant Analysis, and Density Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 611-631, June.
    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).
    5. Hennig, Christian, 2007. "Cluster-wise assessment of cluster stability," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 258-271, September.
    6. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer, vol. 2(1), pages 193-218, December.
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    Cited by:
    1. Marín, J.M. & Rodríguez-Bernal, M.T., 2012. "Multiple hypothesis testing and clustering with mixtures of non-central t-distributions applied in microarray data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1898-1907.

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