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)


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.


Folksonomies Triadic Concept Analysis Closure Operator Equivalence Classes Triadic Concepts 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Besson, J., Robardet, C., Boulicaut, J., Rome, S.: Constraint-based concept mining and its application to microarray data analysis. Intelligent Data Analysis 9, 59–82 (2005)Google Scholar
  2. 2.
    Cerf, L., Besson, J., Robardet, C., Boulicaut, J.: Closed patterns meet n-ary relations. ACM Transactions on Knowledge Discovery from Data 3, 1–36 (2009)CrossRefGoogle Scholar
  3. 3.
    Couch, A.L., Chiarini, M.: A Theory of Closure Operators. In: Hausheer, D., Schönwälder, J. (eds.) AIMS 2008. LNCS, vol. 5127, pp. 162–174. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Ganter, B., Wille, R.: Formal Concept Analysis. Springer (1999)Google Scholar
  5. 5.
    Hamrouni, T., Yahia, S.B., Nguifo, E.M.: Sweeping the disjunctive search space towards mining new exact concise representations of frequent itemsets. Data and Knowledge Engineering 68(10), 1091–1111 (2009)CrossRefGoogle Scholar
  6. 6.
    Jäschke, R., Hotho, A., Schmitz, C., Ganter, B., Stumme, G.: Discovering shared conceptualisations in folksonomies. Web Semantics: Science, Services and Agents on the World Wide Web 6, 38–53 (2008)CrossRefGoogle Scholar
  7. 7.
    Ji, L., Tan, K.L., Tung, A.K.H.: Mining frequent closed cubes in 3d datasets. In: Proceedings of the 32nd International Conference on Very Large Data Bases, Seoul, Korea, pp. 811–822 (2006)Google Scholar
  8. 8.
    Lehmann, F., Wille, R.: A Triadic Approach to Formal Concept Analysis. In: Ellis, G., Rich, W., Levinson, R., Sowa, J.F. (eds.) ICCS 1995. LNCS, vol. 954, pp. 32–43. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  9. 9.
    Mika, P.: Ontologies are us: A unified model of social networks and semantics. Web Semantics: Science, Services and Agents on the World Wide Web 5(1), 5–15 (2007)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Zaki, M.J.: Closed itemset mining and non-redundant association rule mining. In: Liu, L., Ozsu, M.T. (eds.) Encyclopedia of Database Systems. Springer (2009)Google Scholar

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

Personalised recommendations