Quotation Hahsler, Michael, Hornik, Kurt. 2009. Dissimilarity Plots: A Visual Exploration Tool for Partitional Clustering. Department of Statistics and Mathematics, Research Report Series, Report 89.


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Abstract

For hierarchical clustering, dendrograms provide convenient and powerful visualization. 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 paper we extend (dissimilarity) matrix shading with several reordering steps based on seriation. Both methods, matrix shading and seriation, have been well-known for a long time. However, only recent algorithmic improvements allow to use seriation for larger problems. Furthermore, seriation is used in a novel stepwise process (within each cluster and between clusters) which leads to a visualization technique that is independent of the dimensionality of the data. A big advantage is that it presents the structure between clusters and the micro-structure within clusters in one concise plot. This not only allows for judging cluster quality but also makes mis-specification of the number of clusters apparent. We give a detailed discussion of the construction of dissimilarity plots and demonstrate their usefulness with several examples.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Working/discussion paper, preprint
Language English
Title Dissimilarity Plots: A Visual Exploration Tool for Partitional Clustering
Title of whole publication Department of Statistics and Mathematics, Research Report Series, Report 89
Year 2009
URL http://epub.wu-wien.ac.at/dyn/virlib/wp/showentry?ID=epub-wu-01_f3b&from=NEW&style=blank

Associations

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