On Semantic Properties of Interestingness Measures for Extracting Rules from Data

  • Mondher Maddouri
  • Jamil Gammoudi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4431)


The extraction of IF-THEN rules from data is a promising task of data mining including both Artificial Intelligence and Statistics. One of the difficulties encountered is how to evaluate the relevance of the extracted rules? Many authors use statistical interestingness measures to evaluate the relevance of each rule (taken alone). Recently, few research works have done a synthesis study of the existing interestingness measures but their study presents some limits. In this paper, firstly, we present an overview of related works studying more than forty interestingness measures. Secondly, we establish a list of nineteen other interestingness measures not referenced by the related works. Then, we identify twelve semantic properties characterizing the behavior of interestingness measures. Finally, we did a theoretical study of sixty two interestingness measures by outlining their semantic properties. The results of this study are useful to the users of a data-mining system in order to help them to choose an appropriate measure.


Association Rule Semantic Property Logical Implication Interestingness Measure APRIORI Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD Int. Conf. on Management of Data (1993)Google Scholar
  2. 2.
    Azé, J.: Extraction de connaissances à partir de données numériques et textuelles. PhD Thesis, University of Paris-Sud, Paris, France (2003)Google Scholar
  3. 3.
    Blanchard, J.: Un système de visualisation pour l’extraction, l’évaluation, et l’exploration interactives des règles d’association. PhD Thesis, University of Nantes, France (2005)Google Scholar
  4. 4.
    Ben Yahia, S., Gasmi, G., Mephu Nguifo, E., Slimani, Y.: A new informative generic base of association rules. In: Proceedings of the 2nd Int. Workshop on Concept Lattices and Applications (CLA’04), Ostrava, Czech Republic, September 2004, pp. 67–79 (2004)Google Scholar
  5. 5.
    Borgelt, C., Kruse, R.: Induction of association rules: Apriori implementation. In: 15th Conf. on Computational Statistics (2002)Google Scholar
  6. 6.
    Cherfi, H., Toussaint, Y.: Adéquation d’indices statistiques à l’interprétation de règles d’association. In: 6th Int. Conf. On « Analyse statistique des Données Textuelles (JADT) », Saint-Malo, France (March 2002)Google Scholar
  7. 7.
    Gammoudi, M.M.: Méthode de Décomposition Rectangulaire d’une Relation Binaire : une base formelle et uniforme pour la génération automatique des thesaurus et la recherche documentaire. PhD Thesis, Université Sophia-Antipolis, France (1993)Google Scholar
  8. 8.
    Gras, R., Couturier, R., Bernadet, M., Blanchard, J., Briand, H., Guillet, F., Kuntz, P., Lehn, R., Peter, P.: Quelques critères pour une mesure de qualité de règles d’association - un exemple: l’intensité d’implication. National Journal of Information Technologies (RNTI), France (2004)Google Scholar
  9. 9.
    Guillet, F.: Mesures de la qualité des connaissances en ECD, 2004, Tutorial des Journée Extraction et Gestion des Connaissances, EGC (2004)Google Scholar
  10. 10.
    Fukuda, T., Moriomolo, Y., Morichita, S., Tokuyama, T.: Datamining using two-dimensional optimised association rules: Scheme, algorithms and visualisation. In: The ACM-SIGMOD Int. Conf. on the Management of Data, June 1996, ACM Press, New York (1996)Google Scholar
  11. 11.
    Hilderman, R.J., Hamilton, H.J.: Knowledge Discovery and Measures of Interestingness. Kluwer Academic Publishers, Dordrecht (2001)Google Scholar
  12. 12.
    Huynh, X.-H., Guillet, F., Briand, H.: Arqat: an exploratory analysis tool for interestingness measures. In: ASMDA’05, 11th Int. Symposium on Applied Stochastic Models and Data Analysis (2005)Google Scholar
  13. 13.
    Kodratoff, Y.: Comparing Machine Learning and Knowledge Discovery in Databases: An Application to Knowledge Discovery in Texts. LNAI-Tutorial series. Springer, Heidelberg (2000)Google Scholar
  14. 14.
    Lavrač, N., Flach, P.A., Zupan, B.: Rule evaluation measures: A unifying view. In: Džeroski, S., Flach, P.A. (eds.) ILP 1999. LNCS (LNAI), vol. 1634, p. 174. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  15. 15.
    Lehn, R., Guillet, F., Kuntz, P., Briand, H., Philippé, J.: Felix: An interactive rule mining interface in a KDD process. In: Lenca, P. (ed.) HCP’99, pp. 169–174 (1999)Google Scholar
  16. 16.
    Lenca, P., Meyer, P., Vaillant, B., Picouet, P., Lallich, S.: Evaluation et analyse multicritère des mesures de qualité des règles d’associations. National Journal of Information Technologies (RNTI), France, 219–246 (2004)Google Scholar
  17. 17.
    Piatetsky-Shapiro, G.: Discovery, Analysis, and Presentation of Strong Rules. In: Piatetsky-Shapiro, G., Frawley, W.J. (eds.) Knowledge Discovery in Databases, pp. 229–248. MIT Press, Cambridge (1991)Google Scholar
  18. 18.
    Souad-Bensafi, S., LeBourgeois, F., Emptoz, H., Parizeau, M.: La relaxation probabiliste pour l’étiquetage logique des documents: applications aux tables des matières. PhD Thesis, Ottawa, Canada (2001)Google Scholar
  19. 19.
    Totohasina, A., Ralambondrainy, H., Diatta, J.: Notes sur les mesures probabilistes de la qualité des règles d’association: Un algorithme efficace d’extraction es règles d’association implicatives. In: The 7th African Conference on Research in Computer Sciences (CARI’04), Hammamet, Tunisia (November 2004)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Mondher Maddouri
    • 1
  • Jamil Gammoudi
    • 2
  1. 1.Department of Maths & Computer Sciences, National Institute of Applied Sciences & Technology of Tunis – INSAT, University of Carthago, Centre Urbain Nord, B.P. 676, 1080 Tunis Cadex –Tunisia
  2. 2.Department of Computer Sciences, Faculty of Law, Economics and Management of Jendouba – FSJEG, University of Jendouba, Avenue de l’UMA - 8189 Jendouba –Tunisia

Personalised recommendations