A new classification of datasets for frequent itemsets

Journal of Intelligent Information Systems

Volumn 34, Issue 1, 2010, Pages 1-19

  Flouvat, Frédéric ^a   De Marchi, Fabien ^b   Petit, Jean Marc ^c  

^a UNIVERSITÉ DE LA NOUVELLE CALÉDONIE   (New Caledonia)

^b UNIVERSITÉ LYON 1   (France)

^c UNIVERSITÉ DE LYON   (France)

Author keywords

Classification of datasets; Experimental study; Pattern mining

Indexed keywords

DATA MINING;

CONCISE REPRESENTATIONS; EXPERIMENTAL STUDY; ITEM SETS; NEGATIVE BORDERS; PATTERN MINING;

CLASSIFICATION (OF INFORMATION);

EID: 74849129942     PISSN: 09259902     EISSN: 15737675     Source Type: Journal    
DOI: 10.1007/s10844-008-0077-0     Document Type: Article

References (35)

1. Agrawal, R., Imielinski, T., & Swami, A. N. (1993). Mining association rules between sets of items in large databases. In P. Buneman & S. Jajodia (Ed.), SIGMOD Conference (pp. 207-216). New York: ACM.
2. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In J. B. Bocca, M. Jarke, & C. Zaniolo (Ed.), VLDB (pp. 487-499). San Francisco: Morgan Kaufmann.
3. Y. Bastide R. Taouil N. Pasquier G. Stumme L. Lakhal 2000 Mining frequent patterns with counting inference SIGKDD Explorations 2 2 66 75 10.1145/380995.381017
4. Bayardo Jr., R. J., Goethals, B., & Zaki, M. J. (Eds.) (2004). FIMI '04, proceedings of the IEEE ICDM workshop on frequent itemset mining implementations, November 1, 2004, CEUR workshop proceedings (Vol. 126). Brighton, UK: CEUR-WS.org.
5. Bayardo Jr., R. J., & Zaki, M. J. (Eds.) (2003). FIMI '03, proceedings of the IEEE ICDM workshop on frequent itemset mining implementations, November 19, 2003, CEUR workshop proceedings (Vol. 90). Melbourne, Florida, USA: CEUR-WS.org.
6. C. Beeri M. Y. Vardi 1984 A proof procedure for data dependencies Journal of the Association for Computing Machinery 31 4 718 741 0632.68097 819163
7. Borgelt, C. (2003). Efficient implementations of Apriori and Eclat. In R. J. Bayardo Jr., & M. J. Zaki (Eds.), 1st workshop of frequent item set mining implementations. Melbourne, FL, USA: FIMI 2003.
8. J.-F. Boulicaut A. Bykowski C. Rigotti 2003 Free-sets: A condensed representation of boolean data for the approximation of frequency queries Data Mining and Knowledge Discovery 7 1 5 22 10.1023/A:1021571501451 1973704
9. Burdick, D., Calimlim, M., Flannick, J., Gehrke, J., & Yiu, T. (2003). Mafia: A performance study of mining maximal frequent itemsets. In R. J. Bayardo Jr. & M. J. Zaki (Eds.), Journal of Intelligent Information Systems.
10. Burdick, D., Calimlim, M., & Gehrke, J. (2001). Mafia: A maximal frequent itemset algorithm for transactional databases. In ICDE (pp. 443-452). Los Alamitos: IEEE Computer Society.
11. Bykowski, A., & Rigottim, C. (2001). A condensed representation to find frequent patterns. In PODS. New York: ACM.
12. Calders, T., & Goethals, B. (2003). Minimal k-free representations of frequent sets. In N. Lavrac, D. Gamberger, H. Blockeel, & L. Todorovski (Eds.), PKDD. Lecture Notes in Computer Science (Vol. 2838, pp. 71-82). New York: Springer.
13. Casali, A., Cicchetti, R., & Lakhal, L. (2005). Essential patterns: A perfect cover of frequent patterns. In A. Min Tjoa & J. Trujillo (Eds.), DaWaK. Lecture notes in computer science (Vol. 3589, pp. 428-437). New York: Springer.
14. M. A. Casanova R. Fagin C. H. Papadimitriou 1984 Inclusion dependencies and their interaction with functional dependencies Journal of Computer and System Sciences 28 1 29 59 0586.68082 10.1016/0022-0000(84)90075-8 754907 (Pubitemid 14569358)
15. De Marchi, F., & Petit, J.-M. (2003). Zigzag: a new algorithm for mining large inclusion dependencies in database. In ICDM (pp. 27-34). Los Alamitos, IEEE Computer Society.
16. Flouvat, F. (2008). Study of frequent itemsets datasets. http://pages.univ-nc.nc/~flouvat/.
17. Flouvat, F., De Marchi, F., & Petit, J.-M. (2004). ABS: Adaptive Borders Search of frequent itemsets. In R. J. Bayardo Jr., B. Goethals, & M. J. Zaki (Eds.), Journal of Intelligent Information Systems.
18. Goethals, B. (2003). Frequent itemset mining implementations repository. http://fimi.cs.helsinki.fi/.
19. Gouda, K., & Zaki, M. J. (2001). Efficiently mining maximal frequent itemsets. In N. Cercone, T. Y. Lin, & X. Wu (Eds.), ICDM (pp. 163-170). Los Alamitos: IEEE Computer Society.
20. Grahne, G., & Zhu, J. (2003). Efficiently using prefix-trees in mining frequent itemsets. In R. J. Bayardo Jr., B. Goethals, & M. J. Zaki (Eds.), Journal of Intelligent Information Systems.
21. Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. In W. Chen, J. F. Naughton, & P. A. Bernstein (Eds.), SIGMOD conference (pp. 1-12). New York: ACM.
22. M. Kantola H. Mannila K.-J. Räihä H. Siirtola 1992 Discovering functional and inclusion dependencies in relational databases International Journal of Intelligent Systems 7 591 607 0795.68056 10.1002/int.4550070703
23. Koeller, A., & Rundensteiner, E. A. (2003). Discovery of high-dimensional. In U. Dayal, K. Ramamritham, & T. M. Vijayaraman (Eds.), ICDE (pp. 683-685). Los Alamitos: IEEE Computer Society.
24. Kryszkiewicz, M., & Gajek, M. (2002). Concise representation of frequent patterns based on generalized disjunction-free generators. In M.-S. Cheng, P. S. Yu, & B. Liu (Eds.), PAKDD. Lecture notes in computer science (Vol. 2336, pp. 159-171). New York: Springer.
25. Liu, G., Lu, H., Yu, J. X., Wei, W., & Xiao, X. (2003). Afopt: An efficient implementation of pattern growth approach. In R. J. Bayardo Jr., B. Goethals, & M. J. Zaki (Eds.), Journal of Intelligent Information Systems.
26. Mannila, H., & Räihä, K-J. (1994). The design of relational databases (2nd ed.). Reading: Addison-Wesley.
27. Mannila, H., & Toivonen, H. (1996). Multiple uses of frequent sets and condensed representations (extended abstract). In KDD (pp. 189-194).
28. H. Mannila H. Toivonen 1997 Levelwise search and borders of theories in knowledge discovery Data Mining and Knowledge Discovery 1 3 241 258 10.1023/A:1009796218281 (Pubitemid 128000670)
29. Orlando, S., Lucchese, C., Palmerini, P., Perego, R., & Silvestri, F. (2003). kdci: a multi-strategy algorithm for mining frequent sets. In R. J. Bayardo Jr., B. Goethals, & M. J. Zaki (Eds.), Journal of Intelligent Information Systems.
30. Palmerini, P., Orlando, S., & Perego, R. (2004). Statistical properties of transactional databases. In H. Haddad, A. Omicini, R. L. Wainwright, & L. M. Liebrock (Eds.), SAC (pp. 515-519). New York: ACM.
31. Pasquier, N., Bastide, Y., Taouil, R., & Lakhal, L. (1999). Discovering frequent closed itemsets for association rules. In C. Beeri & P. Buneman (Eds.), ICDT. Lecture notes in computer science (Vol. 1540, pp. 398-416). New York: Springer.
32. Ramesh, G., Maniatty, W., & Zaki, M.-J. (2003). Feasible itemset distributions in data mining: theory and application. In PODS (pp. 284-295). New York: ACM.
33. Ramesh, G., Zaki, M.-J., & Maniatty, W. (2005). Distribution-based synthetic database generation techniques for itemset mining. In IDEAS (pp. 307-316). Los Alamitos: IEEE Computer Society.
34. Uno, T., Asai, T., Uchida, Y., & Arimura, H. (2004). An efficient algorithm for enumerating closed patterns in transaction databases. In E. Suzuki, S. Arikawa (Eds.), Discovery Science. Lecture notes in computer science (Vol. 3245, pp. 16-31). New York: Springer.
35. Zaki, M.-J., Parthasarathy, S., Ogihara, M., & Li, W. (1997). New algorithms for fast discovery of association rules. In KDD (pp. 283-286), Journal of Intelligent Information Systems.