Method for Generalization of Fuzzy Sets

P. 273–286.

Frolov D., Mirkin B., Nascimento S., Fenner T.

We define and find a most specific generalization of a fuzzy set of topics assigned to leaves of the rooted tree of a taxonomy. This generalization lifts the set to a “head subject” in the higher ranks of the taxonomy, that is supposed to “tightly” cover the query set, possibly bringing in some errors, both “gaps” and “offshoots”. The method globally minimizes a penalty combining head subjects and gaps and offshoots. We apply this to extract research tendencies from a collection of about 18000 research papers published in Springer journals on data science. We consider a taxonomy of Data Science based on the Association for Computing Machinery Classification of Computing System 2012 (ACM-CCS). We find fuzzy clusters of leaf topics over the text collection and use thematic clusters’ head subjects to make some comments on the tendencies of research.

Keywords: recurrence, generalization, annotated suffix tree, Fuzzy clustering, Spectral clustering

Publication based on the results of: Разработка методов структуризации и концептуализации текстовых данных на основе таксономии предметной области (2019)

In book

International Conference on Artificial Intelligence and Soft Computing. 18th International Conference, ICAISC 2019, Zakopane, Poland, June 16–20, 2019, Proceedings

* 1. Issue 11508. , Cham: Springer, 2019.