How to visualize a crisp or fuzzy topic set over a taxonomy

Mirkin, Boris and Nascimento, S. and Fenner, Trevor and Felizardo, R. (2011) How to visualize a crisp or fuzzy topic set over a taxonomy. In: Kuznetsov, S.O. and Mandal, D.P. and Kundu, M.K. and Pal, S.K (eds.) Pattern Recognition and Machine Intelligence. Lecture Notes in Computer Science 6744 6744. Berlin, Germany: Springer Verlag, pp. 3-12. ISBN 9783642217869.

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Official URL: http://dx.doi.org/10.1007/978-3-642-21786-9_2

Abstract

A novel method for visualization of a fuzzy or crisp topic set is developed. The method maps the set’s topics to higher ranks of the taxonomy tree of the field. The method involves a penalty function summing penalties for the chosen “head subjects” together with penalties for emerging “gaps” and “offshoots”. The method finds a mapping minimizing the penalty function in recursive steps involving two different scenarios, that of ‘gaining a head subject’ and that of ‘not gaining a head subject’. We illustrate the method by applying it to illustrative and real-world data.

Metadata

Item Type:	Book Section
School:	Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
Research Centres and Institutes:	Structural Molecular Biology, Institute of (ISMB)
Depositing User:	Sarah Hall
Date Deposited:	16 May 2013 10:54
Last Modified:	09 Aug 2023 12:33
URI:	https://eprints.bbk.ac.uk/id/eprint/6795

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