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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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    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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