Inductive databases and multiple uses of frequent itemsets: The CINQ approach

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2682, Issue , 2004, Pages 1-23

  Boulicaut, Jean François ^a  

^a UNIVERSITÉ DE LYON   (France)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 33745161578     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-44497-8_1     Document Type: Article

References (75)

1. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proceedings SIGMOD'93, pages 207-216, Washington, USA, May 1993. ACM Press.
2. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo. Fast discovery of association rules. In U. M. Fayyad,.G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pages 307-328. AAAI Press, 1996.
3. E. Baralis and G. Psaila. Incremental refinement of mining queries. In Proceedings DaWaK'99, volume 1676 of LNCS, pages 173-182, Firenze, I, Sept. 1999. Springer-Verlag.
4. Y. Bastide, N. Pasquier, R. Taouil, G. Stumme, and L. Lakhal. Mining minimal non-redundant association rules using frequent closed itemsets. In Proceedings CL 2000, volume 1861 of LNCS, pages 972-986, London, UK, 2000. Springer-Verlag.
5. Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal. Mining frequent patterns with counting inference. SIGKDD Explorations, 2(2):66-75, Dec. 2000.
6. R. J. Bayardo. Efficiently mining long patterns from databases. In Proceedings SIGMOD'98, pages 85-93, Seattle, USA, May 1998. ACM Press.
7. R. J. Bayardo and R. Agrawal. Mining the most interesting rules. In Proceedings SIGKDD'99, pages 145-154, San Diego, USA, Aug. 1999. ACM Press.
8. C. Becquet, S. Blachon, B. Jeudy, J.-F. Boulicaut, and O. Gandrillon. Strong association rule mining for large gene expression data analysis: a case study on human SAGE data. Genome Biology, 3(12), Dec. 2002.
9. M. Botta, J.-F. Boulicaut, C. Masson, and R. Meo. A comparison between query languages for the extraction of association rules. In Proceedings Da WaK'02, volume 2454 of LNCS, pages 1-10, Aix-en-Provence, F, Sept. 2002. Springer-Verlag.
10. M. Botta, J.-F. Boulicaut, C. Masson, and R. Meo. Query languages supporting descriptive rule mining: a comparative study. In P. L. Lanzi and R. Meo, editors, Database Support for Data Mining Applications, number 2682 in LNCS. Springer-Verlag, 2003. This volume.
11. M. Botta, R. Meo, and M.-L. Sapino. Incremental execution of the MINE RULE operator. Technical Report RT 66/2002, Dipartimento di Informatica, Università degli Studi di Torino, Corso Svizzera 185, I-10149 Torino, Italy, May 2002.
12. J.-F. Boulicaut and A. Bykowski. Frequent closures as a concise representation for binary data mining. In Proceedings PAKDD'00, volume 1805 of LNAI, pages 62-73, Kyoto, JP, Apr. 2000. Springer-Verlag.
13. J.-F. Boulicaut, A. Bykowski, and B. Jeudy. Mining association rules with negations. Technical Report 2000-14, INSA Lyon, LISI, Batiment Blaise Pascal, F-69621 Villeurbanne, France, Nov. 2000.
14. J.-F. Boulicaut, A. Bykowski, and B. Jeudy. Towards the tractable discovery of association rules with negations. In Proceedings FQAS'00, Advances in Soft Computing series, pages 425-434, Warsaw, PL, Oct. 2000. Springer-Verlag.
15. J.-F. Boulicaut, A. Bykowski, and C. Rigotti. Approximation of frequency queries by mean of free-sets. In Proceedings PKDD'00, volume 1910 of LNAI, pages 75-85, Lyon, F, Sept. 2000. Springer-Verlag.
16. J.-F. Boulicaut, A. Bykowski, and C. Rigotti. Free-sets: a condensed representation of boolean data for the approximation of frequency queries. Data Mining and Knowledge Discovery journal, 7(1):5-22, 2003.
17. J.-F. Boulicaut and B. Crémilleux. Delta-strong classification rules for predicting collagen diseases. In Proceedings of the ECML-PKDD'01 Discovery Challenge on Thrombosis Data, pages 29-38, Freiburg, D, Sept. 2001. Available on line.
18. J.-F. Boulicaut and B. Jeudy. Using constraint for itemset mining: should we prune or not? In Proceedings BDA'00, pages 221-237, Blois, F, Oct. 2000.
19. J.-F. Boulicaut and B. Jeudy. Mining free-sets under constraints. In Proceedings IDEAS'01, pages 322-329, Grenoble, F, July 2001. IEEE Computer Society.
20. J.-F. Boulicaut, M. Klemettinen, and H. Mannila. Modeling KDD processes within the inductive database framework. In Proceedings DaWaK'99, volume 1676 of LNCS, pages 293-302, Firenze, I, Sept. 1999. Springer-Verlag.
21. D. Braga, A. Campi, S. Ceri, M. Klemettinen, and P. L. Lanzi. Discovering interesting information in XML data with association rules. In Proceedings SAC 2003 Data Mining track, Melbourne, USA, Mar. 2003. ACM Press.
22. D. Braga, A. Campi, M. Klemettinen, and P. L. Lanzi. Mining association rules from XML data. In Proceedings DaWaK'02, volume 2454 of LNCS, pages 21-30, Aix-en-Provence, F, Sept. 2002. Springer-Verlag.
23. S. Brin, R. Motwani, and C. Silverstein. Beyond market baskets: Generalizing association rules to correlations. In Proceedings SIGMOD'97, pages 265-276, Tucson, USA, May 1997. ACM Press.
24. A. Bykowski. Condensed representations of frequent sets: application to descriptive pattern discovery. PhD thesis, Institut National des Sciences Appliquées de Lyon, LISI, F-69621 Villeurbanne cedex, France, Oct. 2002.
25. A. Bykowski and C. Rigotti. A condensed representation to find frequent patterns. In Proceedings PODS'01, pages 267-273. ACM Press, May 2001.
26. T. Calders and B. Goethals. Mining all non derivable frequent itemsets. In Proceedings PKDD'02, volume 2431 of LNAI, pages 74-83, Helsinki, FIN, Aug. 2002. Springer-Verlag.
27. M. Capelle, C. Masson, and J.-F. Boulicaut. Mining frequent sequential patterns under a similarity constraint. In Proceedings IDEAL'02, volume 2412 of LNCS, pages 1-6, Manchester, UK, Aug. 2002. Springer-Verlag.
28. B. Crémilieux and J.-F. Boulicaut. Simplest rules characterizing classes generated by delta-free sets. In Proceedings ES 2002, pages 33-46, Cambridge, UK, Dec. 2002. Springer-Verlag.
29. L. de Raedt. A logical view of inductive databases. Technical report, Institut fur Informatik, Albert-Ludwigs-Universitat, Georges-Kohler-Allee, Gebaude 079, D-79110 Freiburg, Germany, May 2002. 13 pages.
30. L. de Raedt. Query evaluation and optimization for inductive database using version spaces (extended abstract). In Proceedings DTDM'02 co-located with EDBT'02, pages 19-28, Praha, CZ, Mar. 2002. An extended version appears in this volume.
31. L. de Raedt, M. Jaeger, S. D. Lee, and H. Mannila. A theory of inductive query answering (extended abstract). In Proceedings ICDM'02, pages 123-130, Maebashi City, Japan, December 2002. IEEE Computer Press.
32. L. de Raedt and S. Kramer. The levelwise version space algorithm and its application to molecular fragment finding. In Proceedings IJCAI'01, pages 853-862, Seattle, USA, Aug. 2001. Morgan Kaufmann.
33. G. Dong and J. Li. Efficient mining of emerging patterns: Discovering trends and differences. In Proceedings SIGKDD'99, pages 43-52, San Diego, USA, Aug. 1999. ACM Press.
34. M. N. Garofalakis, R. Rastogi, and K. Shim. SPIRIT: Sequential pattern mining with regular expression constraints. In Proceedings VLDB'99, pages 223-234, Edinburgh, UK, September 1999. Morgan Kaufmann.
35. A. Giacommetti, D. Laurent, and C. T. Diop. Condensed representations for sets of mining queries. In Proceedings KDID'02 co-located with ECML-PKDD'02, Helinski, FIN, Aug. 2002. An extended version appears in this volume.
36. B. Goethals and J. V. den Bussche. On supporting interactive association rule mining. In Proceedings DaWaK'00, volume 1874 of LNCS, pages 307-316, London, UK, Sept. 2000. Springer-Verlag.
37. B. Goethals and J. van den Bussche. A priori versus a posteriori filtering of association rules. In Proceedings SIGMOD Workshop DMKD'99, Philadelphia, USA, May 1999.
38. J. Han and M. Kamber. Data Mining: Concepts and techniques. Morgan Kaufmann Publishers, San Francisco, USA, 2000. 533 pages.
39. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In Proceedings ACM SIGMOD'00, pages 1-12, Dallas, Texas, USA, May 2000. ACM Press.
40. H. Hirsh. Theoretical underpinnings of version spaces. In Proceedings IJCAI'91, pages 665-670, Sydney, Australia, Aug. 1991. Morgan Kaufmann.
41. H. Hirsh. Generalizing version spaces. Machine Learning, 17(1):5-46, 1994.
42. T. Imielinski and H. Mannila. A database perspective on knowledge discovery. Communications of the ACM, 39(11):58-64, Nov. 1996.
43. T. Imielinski and A. Virmani. MSQL: A query language for database mining. Data Mining and Knowledge Discovery, 3(4):373-408, 1999.
44. B. Jeudy. Extraction de motifs sous contraintes: application à l'évaluation de requêtes inductives. PhD thesis, Institut National des Sciences Appliquées de Lyon, LISI, F-69621 Villeurbanne cedex, France, Dec. 2002. In French.
45. B. Jeudy and J.-F. Boulicaut. Constraint-based discovery and inductive queries: application to association rule mining. In Proceedings ESF Exploratory Workshop on Pattern Detection and Discovery, volume 2447 of LNAI, pages 110-124, London, UK, Sept. 2002. Springer-Verlag.
46. B. Jeudy and J.-F. Boulicaut. Optimization of association rule mining queries. Intelligent Data Analysis journal, 6:341-357, 2002.
47. B. Jeudy and J.-F. Boulicaut. Using condensed representations for interactive association rule mining. In Proceedings PKDD'02, volume 2431 of LNAI, pages 225-236, Helsinki, FIN, Aug. 2002. Springer-Verlag.
48. S. Kramer. Demand-driven construction of structural features in ILP. In Proceedings ILP'01, volume 2157 of LNCS, pages 132-141, Strasbourg, F, Sept. 2001. Springer-Verlag.
49. S. Kramer and L. de Raedt. Feature construction with version spaces for biochemical applications. In Proceedings ICML'01, pages 258-265, William College, USA, July 2001. Morgan Kaufmann.
50. S. Kramer, L. de Raedt, and C. Helma. Molecular feature mining in HIV data. In Proceedings SIGKDD'01, pages 136-143, San Francisco, USA, Aug. 2001. ACM Press.
51. L. V. Lakshmanan, R. Ng, J. Han, and A. Pang. Optimization of constrained frequent set queries with 2-variable constraints. In Proceedings SIGMOD'99, pages 157-168, Philadelphia, USA, 1999. ACM Press.
52. S. D. Lee and L. de Raedt. Constraint-based mining of first order sequences in SEQLOG. In Proceedings KDID'02 co-located with ECML-PKDD'02, Helsinki, FIN, Aug. 2002. An extended version appears in this volume.
53. B. Liu, W. Hsu, and Y. Ma. Integrating classification and association rule mining. In Proceedings KDD'98, pages 80-86, New York, USA, 1998. AAAI Press.
54. H. Mannila and H. Toivonen. Multiple uses of frequent sets and condensed representations. In Proceedings KDD'96, pages 189-194, Portland, USA, Aug. 1996. AAAI Press.
55. H. Mannila and H. Toivonen. Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery, 1(3):241-258, 1997.
56. C. Masson and F. Jacquenet. Mining frequent logical sequences with SPIRIT-LoG. In Proceedings ILP'02, volume 2583 of LNAI, pages 166-182, Sydney, Australia, July 2002. Springer-Verlag.
57. C. Mellish. The description identification problem. Artificial Intelligence, 52(2):151-168, 1992.
58. R. Meo. Optimization of a language for data mining. In Proceedings of the 18th Symposium on Applied Computing SAC 2003 Data Mining track, Melbourne, USA, Mar. 2003. ACM Press. To appear.
59. R. Meo, G. Psaila, and S. Ceri. An extension to SQL for mining association rules. Data Mining and Knowledge Discovery, 2(2):195-224, 1998.
60. T. Mitchell. Generalization as search. Artificial Intelligence, 18(2):203-226, 1980.
61. P. Moen. Attribute, Event Sequence, and Event Type Similarity Notions for Data Mining. PhD thesis, Department of Computer Science, P.O. Box 26, FIN-00014 University of Helsinki, Jan. 2000.
62. B. Nag, P. M. Deshpande, and D. J. DeWitt. Using a knowledge cache for interactive discovery of association rules. In Proceedings SIGKDD'99, pages 244-253, San Diego, USA, Aug. 1999. ACM Press.
63. C. Nedellec, C. Rouveirol, H. Ade, and F. Bergadano. Declarative bias in inductive logic programming. In L. de Raedt, editor, Advances in Logic Programming, pages 82-103. IOS Press, 1996.
64. R. Ng, L. V. Lakshmanan, J. Han, and A. Pang. Exploratory mining and pruning optimizations of constrained associations rules. In Proceedings SIGMOD'98, pages 13-24, Seattle, USA, 1998. ACM Press.
65. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Efficient mining of association rules using closed itemset lattices. Information Systems, 24(1):25-46, Jan. 1999.
66. J. Pei, G. Dong, W. Zou, and J. Han. On computing condensed frequent pattern bases. In Proceedings ICDM'02, pages 378-385, Maebashi City, JP, Dec. 2002. IEEE Computer Press.
67. J. Pei and J. Han. Constrained frequent pattern mining:a pattern-growth view. SIGKDD Explorations, 4(1):31-39, June 2002.
68. J. Pei, J. Han, and L. V. S. Lakshmanan. Mining frequent itemsets with convertible constraints. In Proceedings ICDE'01, pages 433-442, Heidelberg, D, Apr. 2001. IEEE Computer Press.
69. J. Pei, J. Han, and R. Mao. CLOSET an efficient algorithm for mining frequent closed itemsets. In Proceedings SIGMOD Workshop DMKD'00, Dallas, USA, May 2000.
70. T. Scheffer. Finding association rules that trade support optimally against confidence. In Proceedings PKDD'01, volume 2168 of LNCS, pages 424-435, Freiburg, D, Sept. 2001. Springer-Verlag.
71. J. Sese and S. Morishita. Answering the most correlated N association rules efficiently. In Proceedings PKDD'02, volume 2431 of LNAI, pages 410-422, Helsinki, FIN, Aug. 2002. Springer-Verlag.
72. P. Smyth and R. M. Goodman. An information theoretic approach to rule induction from databases. IEEE Transactions on Knowledge and Data Engineering, 4(4):301-316, Aug. 1992.
73. R. Srikant, Q. Vu, and R. Agrawal. Mining association rules with item constraints. In Proceedings KDD'97, pages 67-73, Newport Beach, USA, 1997. AAAI Press.
74. H. Toivonen. Sampling large databases for association rules. In Proceedings VLDB'96, pages 134-145, Mumbay, India, Sept. 1996. Morgan Kaufmann.
75. M. J. Zaki. Generating non-redundant association rules. In Proceedings SIGKDD'00, pages 34-43, Boston, USA, Aug. 2000. ACM Press.