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Title of Journal: Comput Stat

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Abbravation: Computational Statistics

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Springer-Verlag

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10.1002/hlca.19660490102

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1613-9658

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GEVA geometric variabilitybased approaches for i

Authors: Itziar Irigoien Concepcion Arenas Elena Fernández Francisco Mestres
Publish Date: 2009/10/15
Volume: 25, Issue: 2, Pages: 241-255
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Abstract

This paper arising from population studies develops clustering algorithms for identifying patterns in data Based on the concept of geometric variability we have developed one polytheticdivisive and three agglomerative algorithms The effectiveness of these procedures is shown by relating them to classical clustering algorithms They are very general since they do not impose constraints on the type of data so they are applicable to general economics ecological genetics studies Our major contributions include a rigorous formulation for novel clustering algorithms and the discovery of new relationship between geometric variability and clustering Finally these novel procedures give a theoretical frame with an intuitive interpretation to some classical clustering methods to be applied with any type of data including mixed data These approaches are illustrated with real data on Drosophila chromosomal inversions

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