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How to Visualize a Crisp or Fuzzy Topic Set over a Taxonomy
2011
MiNaFeFe
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.
In proceedings
Boris Mirkin, Susana Nascimento, Trevor Fenner, Rui Felizardo
K. Sergei, D. Mandal, M. Kundu, S. K. Pal
Pattern Recognition and Machine Intelligence
LNCS
Springer-Verlag
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6744
3-12
978-3-642-21785-2
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-
http://www.springerlink.com/content/f6651t5u55p41274/
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Centre for Artificial Intelligence of UNL
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