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Feature Relevance in Ward’s Hierarchical Clustering Using the L p Norm

Feature Relevance in Ward’s Hierarchical Clustering Using the L p Norm In this paper we introduce a new hierarchical clustering algorithm called Ward p . Unlike the original Ward, Ward p generates feature weights, which can be seen as feature rescaling factors thanks to the use of the L p norm. The feature weights are cluster dependent, allowing a feature to have different degrees of relevance at different clusters. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Classification Springer Journals

Feature Relevance in Ward’s Hierarchical Clustering Using the L p Norm

Journal of Classification , Volume 32 (1) – Mar 11, 2015

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References (38)

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/s00357-015-9167-1
Publisher site
See Article on Publisher Site

Abstract

In this paper we introduce a new hierarchical clustering algorithm called Ward p . Unlike the original Ward, Ward p generates feature weights, which can be seen as feature rescaling factors thanks to the use of the L p norm. The feature weights are cluster dependent, allowing a feature to have different degrees of relevance at different clusters.

Journal

Journal of ClassificationSpringer Journals

Published: Mar 11, 2015

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