Quotation Hahsler, Michael, Hornik, Kurt. 2011. Dissimilarity plots: A visual exploration tool for partitional clustering. Journal of Computational and Graphical Statistics 20 (2): 335-354.


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Abstract

For hierarchical clustering, dendrograms are a convenient and powerful visualization technique. Although many visualization methods have been suggested for partitional clustering, their usefulness deteriorates quickly with increasing dimensionality of the data and/or they fail to represent structure between and within clusters simultaneously. In this article we extend (dissimilarity) matrix shading with several reordering steps based on seriation techniques. Both ideas, matrix shading and reordering, have been well known for a long time. However, only recent algorithmic improvements allow us to solve or approximately solve the seriation problem efficiently for larger problems. Furthermore, seriation techniques are used in a novel stepwise process (within each cluster and between clusters) which leads to a visualization technique that is able to present the structure between clusters and the micro-structure within clusters in one concise plot. This not only allows us to judge cluster quality but also makes misspecification of the number of clusters apparent. We give a detailed discussion of the construction of dissimilarity plots and demonstrate their usefulness with several examples. Experiments show that dissimilarity plots scale very well with increasing data dimensionality.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Journal of Computational and Graphical Statistics
Citation Index SCI
WU-Journal-Rating new FIN-A, VW-C
Language English
Title Dissimilarity plots: A visual exploration tool for partitional clustering
Volume 20
Number 2
Year 2011
Page from 335
Page to 354
URL http://pubs.amstat.org/doi/abs/10.1198/jcgs.2010.09139

Associations

People
Hahsler, Michael (Former researcher)
Hornik, Kurt (Details)
Organization
Institut f. Präsides SO (Details)
Institute for Statistics and Mathematics IN (Details)
Research Institute for Computational Methods FI (Details)
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