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A boolean approach for detection of frequent items

  • University of Massachusetts Boston
  • Sabre Inc

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a new technique for identifying frequent patterns that occur in large transactional data sets using Boolean algebras. Our approach, which involves extending the notion of support from sets of attributes to Boolean functions is more expressive than the standard approach that amounts to a search for conjunctive patterns. An efficient algorithm based on computing supports for minterms is presented. Also, we discuss an application of this algorithm to finding independent sets in graphs.

Original languageEnglish
Title of host publicationProceedings - 2017 19th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2017
EditorsTetsuo Ida, Tudor Jebelean, Dana Petcu, Stephen M. Watt, Viorel Negru, Daniela Zaharie
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages173-179
Number of pages7
ISBN (Electronic)9781538626269
DOIs
StatePublished - Nov 9 2018
Event19th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2017 - Timisoara, Romania
Duration: Sep 21 2017Sep 24 2017

Publication series

NameProceedings - 2017 19th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2017

Conference

Conference19th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2017
Country/TerritoryRomania
CityTimisoara
Period9/21/179/24/17

ASJC Scopus Subject Areas

  • Computational Mathematics
  • Computational Theory and Mathematics
  • Software

Keywords

  • Boolean functions
  • Minterms
  • Petersen graph
  • Support

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