Earle, Denise and Hurley, Catherine B. (2011) A New Flexible Dendrogram Seriation Algorithm for Data Visualisation. In: Bulletin of the International Statistical Institute Proceedings of the 58th World Statistics Congress 2011, Dublin. International Statistical Institute, pp. 3284-3293. ISBN 978-90-73592-33-9
Preview
CH-Dendrogram.pdf
Download (599kB) | Preview
Abstract
Seriation is a data analytic tool for obtaining a permutation of a set of objects with the goal
of revealing structural information within the set of objects. Seriating variables, cases or categories
generally improves visualisations of statistical data, for example, by revealing hidden patterns in data
or by making large datasets easier to understand. In this paper we present a new algorithm for seriation
based on dendrograms. Dendrogram seriation algorithms rearrange the nodes in a dendrogram in order
to obtain a permutation of the leaves (i.e. objects) that optimises a given criterion. Our algorithm is
more flexible than currently available seriation algorithms because it allows the user to either choose
from a variety of seriation criteria or to input their own criteria. This choice of seriation criteria is
an important feature because different criteria are suitable for different visualisation settings. Common
seriation criteria include measurements of the path length through a set of objects and measurements
of anti-Robinson form in a symmetric matrix. We propose new seriation criteria called lazy path
length and banded anti-Robinson form, and demonstrate their effectiveness in a variety of visualisation
settings.
Item Type: | Book Section |
---|---|
Additional Information: | Both authors supported by a Research Frontiers Grant from Science Foundation Ireland. |
Keywords: | Visualisation; Seriation; Hierarchical clustering; |
Academic Unit: | Faculty of Science and Engineering > Mathematics and Statistics |
Item ID: | 5553 |
Depositing User: | Dr. Catherine Hurley |
Date Deposited: | 17 Nov 2014 15:25 |
Publisher: | International Statistical Institute |
Refereed: | No |
Funders: | Science Foundation Ireland |
Related URLs: | |
URI: | https://mu.eprints-hosting.org/id/eprint/5553 |
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 |
Repository Staff Only (login required)
Downloads
Downloads per month over past year