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.
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Mirkin, B., Nascimento, S., Fenner, T., Felizardo, R. (2011). How to Visualize a Crisp or Fuzzy Topic Set over a Taxonomy. In: Kuznetsov, S.O., Mandal, D.P., Kundu, M.K., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2011. Lecture Notes in Computer Science, vol 6744. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21786-9_2
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DOI: https://doi.org/10.1007/978-3-642-21786-9_2
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