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DESPOTA: DEndrogram Slicing through a PemutatiOn Test Approach

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  • Dario Bruzzese
  • Domenico Vistocco

Abstract

Hierarchical clustering represents one of the most widespread analytical approaches to tackle classification problems mainly due to the visual powerfulness of the associated graphical representation, the dendrogram. That said, the requirement of appropriately choosing the number of clusters still represents the main difficulty for the final user. We introduce DESPOTA (DEndrogram Slicing through a PermutatiOn Test Approach), a novel approach exploiting permutation tests in order to automatically detect a partition among those embedded in a dendrogram. Unlike the traditional approach, DESPOTA includes in the search space also partitions not corresponding to horizontal cuts of the dendrogram. Applications on both real and syntethic datasets will show the effectiveness of our proposal. Copyright Classification Society of North America 2015

Suggested Citation

  • Dario Bruzzese & Domenico Vistocco, 2015. "DESPOTA: DEndrogram Slicing through a PemutatiOn Test Approach," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 285-304, July.
    • Handle: RePEc:spr:jclass:v:32:y:2015:i:2:p:285-304
    DOI: 10.1007/s00357-015-9179-x
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    References listed on IDEAS

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    1. Weiliang Qiu & Harry Joe, 2006. "Generation of Random Clusters with Specified Degree of Separation," Journal of Classification, Springer;The Classification Society, vol. 23(2), pages 315-334, September.
    2. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    3. Liu, Yufeng & Hayes, David Neil & Nobel, Andrew & Marron, J. S, 2008. "Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 1281-1293.
    4. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    5. Qiu, Weiliang & Joe, Harry, 2006. "Separation index and partial membership for clustering," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 585-603, February.
    6. Romano, Joseph P. & Shaikh, Azeem M. & Wolf, Michael, 2008. "Formalized Data Snooping Based On Generalized Error Rates," Econometric Theory, Cambridge University Press, vol. 24(2), pages 404-447, April.
    7. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    8. Matthijs Warrens, 2008. "On the Equivalence of Cohen’s Kappa and the Hubert-Arabie Adjusted Rand Index," Journal of Classification, Springer;The Classification Society, vol. 25(2), pages 177-183, November.
    9. Glenn Milligan, 1981. "A monte carlo study of thirty internal criterion measures for cluster analysis," Psychometrika, Springer;The Psychometric Society, vol. 46(2), pages 187-199, June.
    10. Park, P.J. & Manjourides, J. & Bonetti, M. & Pagano, M., 2009. "A permutation test for determining significance of clusters with applications to spatial and gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4290-4300, October.
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    Cited by:

    1. Cristina Davino & Rosaria Romano & Domenico Vistocco, 2020. "On the use of quantile regression to deal with heterogeneity: the case of multi-block data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 771-784, December.

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