Visualising multiple overlapping classification hierarchies.

Graham, Martin

Abstract

The revision or reorganisation of hierarchical data sets can result in many possible hierarchical classifications composed of the same or overlapping data sets existing in parallel with each other. These data sets are difficult for people to handle and conceptualise, as they try
to reconcile the different perspectives and structures that such data represents. One area where this situation occurs is the study of botanical taxonomy, essentially the classification and naming of plants. Revisions, new discoveries and new dimensions for classifying plants lead to a proliferation of classifications over the same set of plant data. Taxonomists would like a method of exploring these multiple overlapping hierarchies for interesting information, correlations, or anomalies.
The application and extension of Information Visualisation (IV) techniques, the graphical display of abstract information, is put forward as a solution to this problem. Displaying the multiple classification hierarchies in a visually appealing manner along with powerful interaction mechanisms for examination and exploration of the data allows taxonomists to unearth previously hidden information. This visualisation gives detail that previous visualisations and statistical overviews cannot offer.
This thesis work has extended previous IV work in several respects to achieve this goal. Compact, yet full and unambiguous, hierarchy visualisations have been developed. Linking and brushing techniques have been extended to work on a higher class of structure, namely overlapping trees and hierarchies. Focus and context techniques have been pushed to achieve new effects across the visually distinct representations of these multiple hierarchies.
Other data types, such as multidimensional data and large cluster hierarchies have also been displayed using the final version of the visualisation.

Thesis Type	Thesis
Deposit Date	Oct 2, 2008
Peer Reviewed	Not Peer Reviewed
Keywords	Taxonomy; Hierarchical classification; Overlapping data sets; Information Visualisation; Multidimensional data; Large cluster hierarchies;
Public URL	http://researchrepository.napier.ac.uk/id/eprint/2430
Contract Date	Oct 2, 2008
Award Date	2001-12

Visualising multiple overlapping classification hierarchies.

Graham, Martin

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