Dissimilarity Plots. A Visual Exploration Tool for Partitional Clustering.

Hahsler, Michael and Hornik, Kurt ORCID: https://orcid.org/0000-0003-4198-9911 (2009) Dissimilarity Plots. A Visual Exploration Tool for Partitional Clustering. Research Report Series / Department of Statistics and Mathematics, 89. Department of Statistics and Mathematics, WU Vienna University of Economics and Business, Vienna.


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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.

Item Type: Paper
Keywords: partitional clustering / dissimilarity / visualization / seriation
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Repository Administrator
Date Deposited: 07 Sep 2009 23:02
Last Modified: 24 Oct 2019 13:41
URI: https://epub.wu.ac.at/id/eprint/1244


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