Using Taxonomy Tree to Generalize a Fuzzy Thematic Cluster

This paper presents an algorithm, ParGenFS, for generalizing, or "lifting", a fuzzy set of topics to higher ranks of a hierarchical taxonomy of a research domain. The algorithm ParGenFS finds a globally optimal generalization of the topic set to minimize a penalty function, by balancing th...

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Bibliographic Details
Published inIEEE International Fuzzy Systems conference proceedings pp. 1 - 6
Main Authors Frolov, Dmitry, Nascimento, Susana, Fenner, Trevor, Mirkin, Boris
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2019
Subjects
annotated suffix tree
Artificial intelligence
Clustering algorithms
Computer science
Data science
fuzzy cluster
Fuzzy sets
gap-offshoot penalty
generalization
Taxonomy
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Summary:This paper presents an algorithm, ParGenFS, for generalizing, or "lifting", a fuzzy set of topics to higher ranks of a hierarchical taxonomy of a research domain. The algorithm ParGenFS finds a globally optimal generalization of the topic set to minimize a penalty function, by balancing the number of introduced "head subjects" and related errors, the "gaps" and "offshoots", differently weighted. This leads to a generalization of the topic set in the taxonomy. The usefulness of the method is illustrated on a set of 17685 abstracts of research papers on Data Science published in Springer journals for the past 20 years. We extracted a taxonomy of Data Science from the international Association for Computing Machinery Computing Classification System 2012 (ACM-CCS). We find fuzzy clusters of leaf topics over the text collection, lift them in the taxonomy, and interpret found head subjects to comment on the tendencies of current research.
ISSN:1558-4739
DOI:10.1109/FUZZ-IEEE.2019.8859015