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DESPOTA: DEndrogram Slicing through a PemutatiOn Test Approach

DESPOTA: DEndrogram Slicing through a PemutatiOn Test Approach 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Classification Springer Journals

DESPOTA: DEndrogram Slicing through a PemutatiOn Test Approach

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Publisher
Springer Journals
Copyright
Copyright © 2015 by Classification Society of North America
Subject
Statistics; Statistical Theory and Methods; Pattern Recognition; Bioinformatics; Signal, Image and Speech Processing; Psychometrics; Marketing
ISSN
0176-4268
eISSN
1432-1343
DOI
10.1007/s00357-015-9179-x
Publisher site
See Article on Publisher Site

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.

Journal

Journal of Classification – Springer Journals

Published: Jul 8, 2015

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