Hurley, Catherine B. (2004) Clustering Visualizations of Multidimensional Data. Journal of Computational and Graphical Statistics, 13 (4). pp. 788-806. ISSN 1061-8600

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Abstract

Many graphical methods for displaying multivariate data consist of arrangements of multiple displays of one or two variables; scatterplot matrices and parallel coordinates plots are two such methods. In principle these methods generalize to arbitrary numbers of variables but become difficult to interpret for even moderate numbers of variables. This article demonstrates that the impact of high dimensions is much less severe when the component displays are clustered together according to some index of merit. Effectively, this clustering reduces the dimensionality and makes interpretation easier. For scatterplot matrices and parallel coordinates plots clustering of component displays is achieved by finding suitable permutations of the variables. I discuss algorithms based on cluster analysis for finding permutations, and present examples using various indices of merit.

Item Type:	Article
Keywords:	Parallel coordinates; Permutation of variables; Projection pursuit; Scatterplot matrices;
Academic Unit:	Faculty of Science and Engineering > Mathematics and Statistics
Item ID:	10147
Identification Number:	https://doi.org/10.1198/106186004X12425
Depositing User:	Dr. Catherine Hurley
Date Deposited:	23 Oct 2018 16:16
Journal or Publication Title:	Journal of Computational and Graphical Statistics
Publisher:	American Statistical Association
Refereed:	Yes
URI:
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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