Inferring Knowledge from Concise Representations of Both Frequent and Rare Jaccard Itemsets

  • Souad Bouasker
  • Sadok Ben Yahia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8056)


Correlated pattern mining has become increasingly an important task in data mining and knowledge discovery. Recently, concise exact representations dedicated for frequent correlated and for rare correlated patterns according to the Jaccard measure were presented. In this paper, we offer a new method of inferring new knowledge from the introduced concise representations. A new generic approach, called Gmjp, allowing the extraction of the sets of frequent correlated patterns, of rare correlated patterns and their associated concise representations is introduced. Pieces of new knowledge in the form of associations rules can be either exact or approximate. We also illustrate the efficiency of our approach over several data sets and we prove that Jaccard-based classification rules have very encouraging results.


Concise representation Monotonicity Constraint Correlated pattern Jaccard measure Generic Approach 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases (VLDB 1994), Santiago, Chile, pp. 487–499 (1994)Google Scholar
  2. 2.
    Barsky, M., Kim, S., Weninger, T., Han, J.: Mining flipping correlations from large datasets with taxonomies. In: Proceedings of the 38th International Conference on Very Large Databases, VLDB 2012, Istanbul, Turkey, pp. 370–381 (2012)Google Scholar
  3. 3.
    Ben Younes, N., Hamrouni, T., Ben Yahia, S.: Bridging conjunctive and disjunctive search spaces for mining a new concise and exact representation of correlated patterns. In: Pfahringer, B., Holmes, G., Hoffmann, A. (eds.) DS 2010. LNCS, vol. 6332, pp. 189–204. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Bonchi, F., Lucchese, C.: On condensed representations of constrained frequent patterns. Knowledge and Information Systems 9(2), 180–201 (2006)CrossRefGoogle Scholar
  5. 5.
    Booker, Q.E.: Improving identity resolution in criminal justice data: An application of NORA and SUDA. Journal of Information Assurance and Security 4, 403–411 (2009)Google Scholar
  6. 6.
    Bouasker, S., Hamrouni, T., Ben Yahia, S.: New exact concise representation of rare correlated patterns: Application to intrusion detection. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012, Part II. LNCS, vol. 7302, pp. 61–72. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Ganter, B., Wille, R.: Formal Concept Analysis. Springer (1999)Google Scholar
  8. 8.
    Grahne, G., Lakshmanan, L.V.S., Wang, X.: Efficient mining of constrained correlated sets. In: Proceedings of the 16th International Conference on Data Engineering (ICDE 2000), pp. 512–521. IEEE Computer Society Press, San Diego (2000)CrossRefGoogle Scholar
  9. 9.
    Jaccard, P.: Étude comparative de la distribution orale dans une portion des Alpes et des Jura. Bulletin de la Société Vaudoise des Sciences Naturelles 37, 547–579 (1901)Google Scholar
  10. 10.
    Kim, S., Barsky, M., Han, J.: Efficient mining of top correlated patterns based on null-invariant measures. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part II. LNCS, vol. 6912, pp. 177–192. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Kim, W.-Y., Lee, Y.-K., Han, J.: CCMine: Efficient mining of confidence-closed correlated patterns. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 569–579. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Koh, Y.S., Rountree, N.: Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection. IGI Global Publisher (2010)Google Scholar
  13. 13.
    Le Bras, Y., Lenca, P., Lallich, S.: Mining classification rules without support: an anti-monotone property of jaccard measure. In: Elomaa, T., Hollmén, J., Mannila, H. (eds.) DS 2011. LNCS, vol. 6926, pp. 179–193. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Lee, Y.K., Kim, W.Y., Cai, Y.D., Han, J.: CoMine: efficient mining of correlated patterns. In: Proceedings of the 3rd International Conference on Data Mining (ICDM 2003), pp. 581–584. IEEE Computer Society Press, Melbourne (2003)Google Scholar
  15. 15.
    Mahmood, A.N., Hu, J., Tari, Z., Leckie, C.: Critical infrastructure protection: Resource efficient sampling to improve detection of less frequent patterns in network traffic. Journal of Network and Computer Applications 33(4), 491–502 (2010)CrossRefGoogle Scholar
  16. 16.
    Mannila, H., Toivonen, H.: Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery 3(1), 241–258 (1997)CrossRefGoogle Scholar
  17. 17.
    Manning, A.M., Haglin, D.J., Keane, J.A.: A recursive search algorithm for statistical disclosure assessment. Data Mining and Knowledge Discovery 16(2), 165–196 (2008)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Omiecinski, E.: Alternative interest measures for mining associations in databases. IEEE Transactions on Knowledge and Data Engineering 15(1), 57–69 (2003)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Romero, C., Romero, J.R., Luna, J.M., Ventura, S.: Mining rare association rules from e-learning data. In: Proceedings of the 3rd International Conference on Educational Data Mining (EDM 2010), Pittsburgh, PA, USA, pp. 171–180 (2010)Google Scholar
  20. 20.
    Segond, M., Borgelt, C.: Item set mining based on cover similarity. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part II. LNCS, vol. 6635, pp. 493–505. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  21. 21.
    Soulet, A., Raissi, C., Plantevit, M., Crémilleux, B.: Mining dominant patterns in the sky. In: Proceedings of the 11th IEEE International Conference on Data Mining, ICDM 2011, Vancouver, Canada, pp. 655–664 (2011)Google Scholar
  22. 22.
    Surana, A., Kiran, R.U., Reddy, P.K.: Selecting a right interestingness measure for rare association rules. In: Proceedings of the 16th International Conference on Management of Data (COMAD 2010), Nagpur, India, pp. 115–124 (2010)Google Scholar
  23. 23.
    Szathmary, L., Valtchev, P., Napoli, A.: Generating rare association rules using the minimal rare itemsets family. International Journal of Software and Informatics 4(3), 219–238 (2010)Google Scholar
  24. 24.
    Tanimoto, T.T.: An elementary mathematical theory of classification and prediction. Technical Report, I.B.M. Corporation Report (1958)Google Scholar
  25. 25.
    Tsang, S., Koh, Y.S., Dobbie, G.: RP-tree: Rare pattern tree mining. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2011. LNCS, vol. 6862, pp. 277–288. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  26. 26.
    Wu, T., Chen, Y., Han, J.: Re-examination of interestingness measures in pattern mining: a unified framework. Data Mining and Knowledge Discovery 21, 371–397 (2010)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Souad Bouasker
    • 1
  • Sadok Ben Yahia
    • 1
    • 2
  1. 1.Computer Science Department, Faculty of Sciences of TunisLIPAHTunisTunisia
  2. 2.Institut Telecom, Telecom SudParis, UMR 5157, CNRS SamovarFrance

Personalised recommendations