Towards a Unifying Probabilistic Implicative Normalized Quality Measure for Association Rules

1. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In P. Buneman and S. Jajodia, editors, Proc. of the ACM SIGMOD International Conference on Management of Data, volume 22, pages 207-216, Washington, 1993. ACM press.

   Google Scholar 

2. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. of the 20th VLDB Conference, pages 487-499, 1994.

   Google Scholar 

3. J. Azé. Une nouvelle mesure de qualité pour l’extraction de pépites de connaissances. Revue des Sciences et Technologies de l’Information, 17:171-182, 2003.

   Google Scholar 

4. R. J. Bayardo. Efficiently mining long patterns from databases. In Proc. of the ACM SIGMOD Conference, pages 85-93, 1998.

   Google Scholar 

5. J. Blanchard, F. Guillet, H. Brilland, and R. Gras. Assessing rule interestigness with a probabilistic measure of deviation from equilibrium. In Proc. of Applied stochastic Models and Data Analysis, pages 334-344, ENST Bretagne, France, 2005.

   Google Scholar 

6. S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. Dynamic itemset counting and implication rules for market basket data. In Proc. of the ACM SIGMOD Conference, pages 255-264, 1997.

   Google Scholar 

7. D. Feno, J. Diatta, and A. Totohasina. Normalisée d’une mesure probabiliste de qualité des règles d’association: étude de cas. In Actes du 2nd Atelier Qualité des Données et des Connaissances, pages 25-30, Lille, France, 2006.

   Google Scholar 

8. A.A. Freitas. On rule interestingness measure. Knowledge-Based System, 12:309-315, 1999.

   Article  Google Scholar 

9. R. Gras. L’implication statistique. Nouvelle méthode exploratoire de données. La Penée sauvage, France, 1996.

   Google Scholar 

10. S. Guillaume. Traitement des données volumineuses. Mesures et algorithmes d’extraction des règles d’association et règles ordinales. PhD thesis, Université de Nantes, France, 2000.

    Google Scholar 

11. R. J. Hilderman and H. J. Hamilton. Knowledge discovery and interestingness measures: A survey. Technical Report CS 99-04, Department of Computer Science, University of Regina, 1999.

    Google Scholar 

12. J. Hipp, U. Güntzer, and G. Nakhaeizadeh. Algorithms for Association Rule Mining - a General Survey and Comparison. SIGKDD Explorations, 2:58-64, 2000.

    Article  Google Scholar 

13. ıane. Mining positive and negative Association Rules: an approach for confined rules. Technical report, Dept of Computing Sciences, university of Alberta, Canada, 2004.

    Google Scholar 

14. Nicolas Pasquier, Yves Bastide, Rafik Taouil, and Lotfi Lakhal. Closed set based discovery of small covers for association rules. In Proc. 15emes Journees Bases de Donnees Avancees, BDA, pages 361-381, 1999.

    Google Scholar 

15. G. Piatetsky-Shapiro. Knowledge discovery in real databases. a report on the ijcai-86 workshop. AI Magazine, 11(5):68-70, 1991.

    Google Scholar 

16. A Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. In Proc. of the 21th VLDB Conference, pages 432-444, September 1995.

    Google Scholar 

17. H. Toivonen. Sampling large databases for association rules. In Proc. of the 22nd VLDB Conference, pages 134-145, September 1994.

    Google Scholar 

18. A. Totohasina. Notes sur l’implication statistique: dépendance positive orientée, valeurs critiques. Technical report, SCAD, Dept de Maths-Info, Université du Québec à Montréal, 1994.

    Google Scholar 

19. A. Totohasina. Normalization of probabilistic quality measure (in french). In Proc. French Society of Statistics (SFDS’03), XXVth Days of Statistics, volume 2, pages 958-988, Lyon 2, France, 2003.

    Google Scholar 

20. X. Wu, C. Zhang, and S. Zhang. Mining both positive and negative rules. ACM J. Information Systems, 22(3):381-405, 2004.

    Google Scholar 

21. M. J. Zaki and C.-J. Hsiao. CHARM: An efficient algorithm for closed itemset mining, 1999. Technical Report 99-10, Computer Science, Rensselaer Polytechnic Institute, 1999.

    Google Scholar 

22. M. J. Zaki and M. Ogihara. Theoretical Foundations of Association Rules. In 3rd SIGMOD’98 Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD), pages 1-8, 1998.

    Google Scholar 

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