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

Renato Amorim ()

Journal of Classification, 2015, vol. 32, issue 1, 46-62

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. We validate our method by performing experiments on a total of 75 real-world and synthetic datasets, with and without added features made of uniformly random noise. Our experiments show that: (i) the use of our feature weighting method produces results that are superior to those produced by the original Ward method on datasets containing noise features; (ii) it is indeed possible to estimate a good exponent p under a totally unsupervised framework. The clusterings produced by Ward p are dependent on p. This makes the estimation of a good value for this exponent a requirement for this algorithm, and indeed for any other also based on the L p norm. Copyright Classification Society of North America 2015

Keywords: Ward method; Hierarchical clustering; Feature weights; Feature relevance; L p norm; Minkowski metric. (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s00357-015-9167-1

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