The model of most informative patterns and its application to knowledge extraction from graph databases

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

Volumn 5782 LNAI, Issue PART 2, 2009, Pages 205-220

  Pennerath, Frédéric ^a,b   Napoli, Amedeo ^b  

^a SUPELEC   (France)

^b LORIA   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATABASE SYSTEMS;

DOMAIN KNOWLEDGE; FORMAL PROPERTIES; INFORMATIVE PATTERNS; ITS APPLICATIONS; KNOWLEDGE EXTRACTION; MOLECULAR GRAPHS; SCORING FUNCTIONS; SECOND SCREENS;

LEARNING SYSTEMS;

EID: 70350026511     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04174-7_14     Document Type: Conference Paper

References (21)

1. Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., pp. 207-216. ACM Press, New York (1993)
2. Nijssen, S., Kok, J.N.: A quickstart in frequent structure mining can make a difference. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, Washington, USA, pp. 647-652. ACM Press, New York (2004)
3. Soulet, A., Cŕemilleux, B.: Extraction des top-k motifs par approximer-et-pousser. In: Noirhomme-Fraiture, M., Venturini, G. (eds.) Extraction et gestion des connaissances (EGC 2007), Actes des cinquìemes jourńees Extraction et Gestion des Connaissances, Namur, Belgique. Volume RNTI-E-9 of Revue des Nouvelles Technologies de l'Information, Ćepadùes-́Editions, January 23-26, vol.2, pp. 271-282 (2007)
4. Siebes, A., Vreeken, J., van Leeuwen, M.: Item sets that compress. In: Ghosh, J., Lambert, D., Skillicorn, D.B., Srivastava, J. (eds.) SDM. SIAM, Philadelphia (2006)
5. Cook, D.J., Holder, L.B.: Substructure discovery using minimum description length and background knowledge. J. of Art. Intell. Res. 1, 231-255 (1994)
6. Raedt, L.D., Guns, T., Nijssen, S.: Constraint programming for itemset mining. In: Li, Y., Liu, B., Sarawagi, S. (eds.) KDD, pp. 204-212. ACM, New York (2008)
7. Pennerath, F., Napoli, A.: La famille des motifs les plus informatifs. application ̀a l'extraction de graphes en chimie organique. Revue I3 8(2), 252 (2008)
8. McKay, B.D.: Practical graph isomorphism. Congr. Numer. 30, 45-87 (1981)
9. Zaki, M.J.: Scalable algorithms for association mining. IEEE T. Knowl. Data. En. 12(3), 372-390 (2000)
10. Borgelt, C., Berthold, M.R.: Mining molecular fragments: Finding relevant substructures of molecules. In: Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002), Maebashi City, Japan, vol.51. IEEE Computer Society, Los Alamitos (2002)
11. Tarjan, R.E.: Depth-.rst search and linear graph algorithms. SIAM J. Comput. 1(2), 146-160 (1972)
12. Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Min. Knowl. Discov. 8(1), 53-87 (2004)
13. Brijs, T., Swinnen, G., Vanhoof, K., Wets, G.: Using association rules for product assortment decisions: A case study. In: KDD, pp. 254-260 (1999)
14. Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Eficient mining of association rules using closed itemset lattices. International Journal of Information Systems 24(1), 25-46 (1999)
15. Boulicaut, J.F., Bykowski, A., Rigotti, C.: Free-sets: A condensed representation of boolean data for the approximation of frequency queries. Data Min. Knowl. Discov. 7(1), 5-22 (2003)
16. Soulet, A., Cŕemilleux, B.: Adequate condensed representations of patterns. Data Min. Knowl. Discov. 17(1), 94-110 (2008)
17. Yan, X., Cheng, H., Han, J., Xin, D.: Summarizing itemset patterns: a pro.lebased approach. In: Grossman, R., Bayardo, R.J., Bennett, K.P. (eds.) KDD, pp. 314-323. ACM, New York (2005)
18. Chen, C., Lin, C.X., Yan, X., Han, J.: On effective presentation of graph patterns: a structural representative approach. In: Shanahan, J.G., Amer-Yahia, S., Manolescu, I., Zhang, Y., Evans, D.A., Kolcz, A., Choi, K.S., Chowdhury, A. (eds.) CIKM, pp. 299-308. ACM, New York (2008)
19. Gallo, A., Bie, T.D., Cristianini, N.: Mini: Mining informative non-redundant itemsets. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenic, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol.4702, pp. 438-445. Springer, Heidelberg (2007)
20. Hasan, M.A., Chaoji, V., Salem, S., Besson, J., Zaki, M.J.: Origami: Mining representative orthogonal graph patterns. In: Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), pp. 153-162. IEEE Computer Society, Los Alamitos (2007)
21. Bringmann, B., Zimmermann, A.: One in a million: picking the right patterns. Knowledge and Information Systems (2008)