Emulating a cooperative behavior in a generic association rule visualization tool

Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

Volumn , Issue , 2004, Pages 148-155

  Ben Yahia, Sadok ^a   Nguifo, Engelbert Mephu ^b  

^a FACULTÉ DES SCIENCES DE TUNIS   (Tunisia)

^b UNIVERSITÉ D'ARTOIS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER ARCHITECTURE; COMPUTER GRAPHICS; COMPUTER HARDWARE; DATABASE SYSTEMS; DECISION MAKING; HUMAN COMPUTER INTERACTION; USER INTERFACES; VISUALIZATION;

GENERIC BASES; GRAPHICAL VISUALIZATION; INTRINSIC STRUCTURE; VISUALIZATION TOOLS;

DATA MINING;

EID: 16244421052     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. P. Adriaans and D. Zantinge. Data mining. Addison-Wesley Longman, 1997.
2. R. Agrawal and R. Skirant. Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Databases, pages 478-499, June 1994.
3. M. Barbut and B. Monjardet. Ordre et classification. Algèbre et Combinatoire. Hachette, Tome II, 1970.
4. Y. Bastide, N. Pasquier, R. Taouil, L. Lakhal, and G. Stumme. Mining minimal non-redundant association rules using frequent closed itemsets. In Proceedings of the International Conference DOOD'2000, Lecture Notes in Computer Sciences, Springer-verlag, pages 972-986, July 2000.
5. R. Belohlavek. Fuzzy Galois connections. Math. Logic, 45(4):497-504, 1999.
6. S. BenYahia and A. Jaoua. Discovering knowledge from fuzzy concept lattice. In Data mining and Computational Intelligence, Studies in Fuzziness and Soft Computing, Vol. 68, chapter 7, pages 167-190. Physica Verlag, Heidelberg, March 2001.
7. S. BenYahia and E. M. Nguifo. Approches d'extraction de règles d'association basées sur la correspondance de galois. Ingénierie des Systèmes d'Information (ISI), 3-4(9):23-55, 2004.
8. S. BenYahia and E. M. Nguifo. Revisiting generic bases of association rules. In Proceedings of 6th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2004) (Springer-Verlag) to appear, Zaragoza, Spain, 2004.
9. S. BenYahia and Y. Slimani. Discovering Association Rules using the Formal Concept Analysis. Extraction des Connaissances et Apprentissage (special issue of Intl. Conference of Journé es francophones d'Extraction et Gestion des Connaissances(EGC'2002), Montpellier, France, 1(4):233-244, January 2002.
10. S. Brin, R. Motawni, and C. Silverstein. Beyond market baskets : Generalizing association rules to correlation. In Proceedings of the SIGMOD, Tucson, Arizona (USA), pages 265-276, May 1997.
11. S. Brin, R. Motwani, and J. Ullman. Dynamic itemset counting and implication rules. In Proceedings ACM SIGMOD, International conference on Management of Data, Tucson, Arizona, USA, pages 255-264, 1997.
12. P. Buono, M. F. Costabile, and F. A. Lisi. Supporting data analysis through visualizations. In Proceedings of Workshop on Visual Data Mining, Freiburg, Germany, pages 67-78, September 2001.
13. O. Couturier, E. M. Nguifo, and B. Noiret. Recherche de règles d'associations hiérarchiques par une approche anthropocentrée. Revue d'Intelligence Artificielle (special issue of Intl. Conference of Journées francophones d'Extraction et Gestion des Connaissances(EGC'2004), Clermont-Ferrand.France, Cepaduès-Editions, (1-2):567-572, 19-23 January 2004.
14. F. Cuppens and R. Demolombe. How to recognize interesting topics to provide cooperative answering. Information Systems, 14(2):163-173, 1989.
15. V. Frantz and J. Shapiro. Algorithms for automatic construction of query formulations in boolean form. Journal of the American Society for Information Science, 1(42): 16-26, 1991.
16. B. Ganter and R. Wille. Formal Concept Analysis. Springer-Verlag, Heidelberg, 1999.
17. J. Guigues and V. Duquenne. Families minimales d'implications informatives résultant d'un tableau de données binaires. Mathématiques et Sciences Humaines, (95):5-18, 1986.
18. J. Han and Y. Fu. Discovery of multiple-level association rules from large databases. In Proceedings of the VLDB Conference, pages 420-431, 1995.
19. A. Jaoua, S. Elloumi, S. BenYahia, and F. Alvi. Galois connection in fuzzy binary relations: applications for discovering association rules and decision making. Methodes Publisher, 2002.
20. M. Kryszkiewicz. Concise representations of association rules. In D. J. Hand, N. Adams, and R. Bolton, editors, Proceedings of Pattern Detection and Discovery, ESF Exploratory Workshop, London, UK, volume 2447 of Lecture Notes in Computer Science, pages 92-109. Springer, September 2002.
21. M. Luxenburger. Implication partielles dans un contexte. Mathématiques et Sciences Humaines, 29(113):35-55, 1991.
22. H. Manilla, H. Toinoven, and I. Verkamo. Efficient algorithms for discovering association rules. In AAAI Worshop on Knowledge Discovery in Databases, pages 181-192, July 1994.
23. R. Meo, G. Psaila, and S. Ceri. A new sql-like operator for mining association rules. In Proceedings of the VLDB Conference, pages 122-133, 1996.
24. A. Motro. Extending the relational Databases Model to support Goal queries. In L. Lerschberg, editor, Expert Database Systems, pages 129-150. The Benjamin/Cummings Publishing Company, 1987.
25. R. T. Ng, V. S. Lakshmanan, J. han, and A. Pang. Exploratory mining and pruning optimizations of constrained association rules. In Proceedings of the SIGMOD Conference, pages 13-24, 1998.
26. S. Pollandt. Fuzzy-Begriffe: Formale Begriffsanalyse unscharfer Daten. Springer-verlag, 1997.
27. B. Shneiderman. The eyes have it: A task by data type taxonomy for information visualization. In I. C. S. Press, editor, Proceedings IEEE Symposium on Visual Languages, Boulder, Colorado. 336-343, 1996.
28. R. Srikant, Q. Vu, and R. Agrawal. Mining association rules with item constraints. In Proc. of the 3rd Intl. Conference on Knowledge Discovery in Databases and Data Mining, Newport Beach, California, pages 67-73, August 1997.