Method for Generalization of Fuzzy Sets

Dmitry Frolov, Boris Mirkin, Susana Nascimento, Trevor Fenner

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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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.

Original languageEnglish
Title of host publicationArtificial Intelligence and Soft Computing - 18th International Conference, ICAISC 2019, Proceedings
EditorsLeszek Rutkowski, Rafał Scherer, Marcin Korytkowski, Ryszard Tadeusiewicz, Witold Pedrycz, Jacek M. Zurada
Place of PublicationCham
Number of pages14
ISBN (Electronic)000485150200026
ISBN (Print)978-3-030-20911-7
Publication statusPublished - 2019
Event18th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2019 - Zakopane, Poland
Duration: 16 Jun 201920 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11508 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference18th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2019


  • Annotated Suffix Tree
  • Fuzzy cluster
  • Generalization
  • Recurrence
  • Spectral clustering


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