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Globally Optimal Parsimoniously Lifting a Fuzzy Query Set Over a Taxonomy Tree

  • Dmitry FrolovEmail author
  • Boris Mirkin
  • Susana Nascimento
  • Trevor Fenner
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 991)

Abstract

This paper presents a relatively rare case of an optimization problem in data analysis to admit a globally optimal solution by a recursive algorithm. We are concerned with finding a most specific generalization of a fuzzy set of topics assigned to leaves of domain taxonomy represented by a rooted tree. The idea is to “lift” the set to its “head subject” in the higher ranks of the taxonomy tree. The head subject is supposed to “tightly” cover the query set, possibly bringing in some errors, either “gaps” or “offshoots” or both. Our method globally minimizes a penalty function combining the numbers of head subjects and gaps and offshoots, differently weighted. We apply this to a collection of 17645 research papers on Data Science published in 17 Springer journals for the past 20 years. We extract a taxonomy of Data Science (TDS) from the international Association for Computing Machinery Computing Classification System 2012. We find fuzzy clusters of leaf topics over the text collection, optimally lift them to head subjects in TDS, and comment on the tendencies of current research following from the lifting results.

Keywords

Hierarchical taxonomy Parsimony Generalization Additive fuzzy cluster Spectral clustering Annotated suffix tree 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Dmitry Frolov
    • 1
    Email author
  • Boris Mirkin
    • 1
    • 2
  • Susana Nascimento
    • 3
  • Trevor Fenner
    • 2
  1. 1.Department of Data Analysis and Artificial IntelligenceNational Research University Higher School of EconomicsMoscowRussian Federation
  2. 2.Department of Computer Science and Information SystemsBirkbeck University of LondonLondonUK
  3. 3.Department of Computer Science and NOVA LINCSUniversidade Nova de LisboaCaparicaPortugal

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