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Scalable Mining of Frequent Tri-concepts from Folksonomies

  • Chiraz Trabelsi
  • Nader Jelassi
  • Sadok Ben Yahia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7302)

Abstract

Mining frequent tri-concepts from folksonomies is an interesting problem with broad applications. Most of the previous tri-concepts mining based algorithms avoided a straightforward handling of the triadic contexts and paid attention to an unfruitful projection of the induced search space into dyadic contexts. As a such projection is very computationally expensive since several tri-concepts are computed redundantly, scalable mining of folksonomies remains a challenging problem. In this paper, we introduce a new algorithm, called Tricons, that directly tackles the triadic form of folksonomies towards a scalable extraction of tri-concepts. The main thrust of the introduced algorithm stands in the application of an appropriate closure operator that splits the search space into equivalence classes for the the localization of tri-minimal generators. These tri-minimal generators make the computation of the tri-concepts less arduous than do the pioneering approches of the literature.The experimental results show that the Tricons enables the scalable frequent tri-concepts mining over two real-life folksonomies.

Keywords

Folksonomies Triadic Concept Analysis Closure Operator Equivalence Classes Triadic Concepts 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Chiraz Trabelsi
    • 1
  • Nader Jelassi
    • 1
  • Sadok Ben Yahia
    • 1
    • 2
  1. 1.Faculty of Sciences of TunisUniversity Tunis El-ManarTunisTunisia
  2. 2.Institut TELECOM, TELECOM SudParis, UMR 5157 CNRS SamovarEvry CedexFrance

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