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Recombining partitions from multivariate data: a clustering method on Bayes factors

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  • Adolfo Álvarez

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  • Daniel Peña

    ()

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    Abstract

    We introduce SAGRA (Split And Group Recombining Algorithm), a cluster analysis methodology which split the data set into small homogeneous groups and later recombine those groups using Bayes factors. We compare the performance of SAGRA with other three cluster analysis algorithms: SAR, M-clust and K-means, using five quality measures: Purity, number of groups, Rand index, adjusted Rand index, and F1, over four different data configurations. Results indicate that the SAGRA algorithm obtain consistently similar or better indexes than the other algorithms over all measures and data configurations

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    File URL: http://e-archivo.uc3m.es/bitstream/handle/10016/18726/ws140804.pdf?sequence=1
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    Bibliographic Info

    Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws1450804.

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    Date of creation: Mar 2014
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    Handle: RePEc:cte:wsrepe:ws1450804

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    Keywords: Cluster analysis; Bayes factors; SAR; SAGRA;

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    1. Atkinson, A.C. & Riani, M., 2007. "Exploratory tools for clustering multivariate data," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 272-285, September.
    2. Christian Hennig, 2010. "Methods for merging Gaussian mixture components," Advances in Data Analysis and Classification, Springer, vol. 4(1), pages 3-34, April.
    3. Mark Culp, . "spa: Semi-Supervised Semi-Parametric Graph-Based Estimation in R," Journal of Statistical Software, American Statistical Association, vol. 40(i10).
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