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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
-
6744
3-12
978-3-642-21785-2
-
-
http://www.springerlink.com/content/f6651t5u55p41274/
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Boris Mirkin and Susana Nascimento and Trevor Fenner and Rui Felizardo, How to Visualize a Crisp or Fuzzy Topic Set over a Taxonomy, in: K. Sergei and D. Mandal and M. Kundu and S. K. Pal (eds), Pattern Recognition and Machine Intelligence, LNCS, Springer-Verlag, Vol. 6744, ISBN 978-3-642-21785-2, Pag. 3-12, (http://www.springerlink.com/content/f6651t5u55p41274/), 2011.
Boris Mirkin, <a href="/people/members/view.php?code=4d69262d034cb8174d039bea8d970836" class="author">Susana Nascimento</a>, Trevor Fenner and Rui Felizardo, <b>How to Visualize a Crisp or Fuzzy Topic Set over a Taxonomy</b>, in: K. Sergei, D. Mandal, M. Kundu and S. K. Pal (eds), <u>Pattern Recognition and Machine Intelligence</u>, LNCS, Springer-Verlag, Vol. 6744, ISBN 978-3-642-21785-2, Pag. 3-12, (<a href="http://www.springerlink.com/content/f6651t5u55p41274/" target="_blank">url</a>), 2011.
@inproceedings {MiNaFeFe, author = {Boris Mirkin and Susana Nascimento and Trevor Fenner and Rui Felizardo}, editor = {K. Sergei and D. Mandal and M. Kundu and S. K. Pal}, title = {How to Visualize a Crisp or Fuzzy Topic Set over a Taxonomy}, booktitle = {Pattern Recognition and Machine Intelligence}, series = {LNCS}, publisher = {Springer-Verlag}, volume = {6744}, pages = {3-12}, isbn = {978-3-642-21785-2}, url = {http://www.springerlink.com/content/f6651t5u55p41274/}, 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.}, year = {2011}, }
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