Accelerating Effect of Attribute Variations: Accelerated Gradual Itemsets Extraction

  • Amal Oudni
  • Marie-Jeanne Lesot
  • Maria Rifqi
Part of the Communications in Computer and Information Science book series (CCIS, volume 443)


Gradual itemsets of the form “the more/less A, the more/less B” summarize data through the description of their internal tendencies, identified as correlation between attribute values. This paper proposes to enrich such gradual itemsets by taking into account an acceleration effect, leading to a new type of gradual itemset of the form “the more/less A increases, the more quickly B increases”. It proposes an interpretation as convexity constraint imposed on the relation between A and B and a formalization of these accelerated gradual itemsets, as well as evaluation criteria. It illustrates the relevance of the proposed approach on real data.


Gradual Itemset Acceleration Enrichment Convexity 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Galichet, S., Dubois, D., Prade, H.: Imprecise specification of illknown functions using gradual rules. Int. Journal of Approximate Reasoning 35, 205–222 (2004)CrossRefzbMATHMathSciNetGoogle Scholar
  2. 2.
    Hüllermeier, E.: Implication-based fuzzy association rules. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 241–252. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  3. 3.
    Dubois, D., Prade, H.: Gradual inference rules in approximate reasoning. In: Proc. of the Int. Conf. on Fuzzy Systems, vol. 61, pp. 103–122 (1992)Google Scholar
  4. 4.
    Hüllermeier, E.: Association rules for expressing gradual dependencies. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 200–211. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Berzal, F., Cubero, J.C., Sanchez, D., Vila, M.A., Serrano, J.M.: An alternative approach to discover gradual dependencies. Int. Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, 559–570 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  6. 6.
    Laurent, A., Lesot, M.-J., Rifqi, M.: GRAANK: Exploiting rank correlations for extracting gradual itemsets. In: Andreasen, T., Yager, R.R., Bulskov, H., Christiansen, H., Larsen, H.L. (eds.) FQAS 2009. LNCS, vol. 5822, pp. 382–393. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Di Jorio, L., Laurent, A., Teisseire, M.: Fast extraction of gradual association rules: a heuristic based method. In: Proc. of the 5th Int. Conf. on Soft Computing as Transdisciplinary Science and Technology, pp. 205–210 (2008)Google Scholar
  8. 8.
    Di-Jorio, L., Laurent, A., Teisseire, M.: Mining frequent gradual itemsets from large databases. In: Adams, N.M., Robardet, C., Siebes, A., Boulicaut, J.-F. (eds.) IDA 2009. LNCS, vol. 5772, pp. 297–308. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Bouchon-Meunier, B., Laurent, A., Lesot, M.-J., Rifqi, M.: Strengthening fuzzy gradual rules through “all the more” clauses. In: Proc. of the Int. Conf. on Fuzzy Systems, pp. 1–7 (2010)Google Scholar
  10. 10.
    Oudni, A., Lesot, M.-J., Rifqi, M.: Characterisation of gradual itemsets through “especially if” clauses based on mathematical morphology tools. In: EUSFLAT, pp. 826–833 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Amal Oudni
    • 1
    • 2
  • Marie-Jeanne Lesot
    • 1
    • 2
  • Maria Rifqi
    • 3
  1. 1.Sorbonne Universités, UPMC Univ Paris 06, UMR 7606 LIP6ParisFrance
  2. 2.CNRS, UMR 7606, LIP6ParisFrance
  3. 3.Université Panthéon-Assas - Paris 02, LEMMAParisFrance

Personalised recommendations