Mining frequent gradual itemsets from large databases

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

Volumn 5772 LCNS, Issue , 2009, Pages 297-308

  Di Jorio, Lisa ^a   Laurent, Anne ^a   Teisseire, Maguelonne ^b  

^a LIRMM   (France)

^b UMR TETIS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL DATABASE; DATA STREAM; EXPERIMENTAL STUDIES; GRADUAL RULES; ITEM SETS; LARGE DATABASE; NUMERICAL DATA; REAL DATA SETS; REAL-WORLD APPLICATION; SCALABLE METHODS; SENSOR READINGS;

ASSOCIATIVE PROCESSING; DATA MINING; DATABASE SYSTEMS; SCALABILITY;

FUZZY LOGIC;

EID: 70349847747     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-03915-7_26     Document Type: Conference Paper

References (12)

1. Srikant, R., Agrawal, R.: Mining Quantitative Association Rules in Large Relational Tables. In: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, pp. 1-12 (1996
2. Hüllermeier, E.: Association rules for expressing gradual dependencies. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol.2431, pp. 200-211. Springer, Heidelberg (2002
3. Berzal, F., Cubero, J.C., Sanchez, D., Vila, M.A., Serrano, J.M.: An alternative approach to discover gradual dependencies. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS) 15(5), 559-570 (2007
4. Dubois, D., Prade, H.: Gradual elements in a fuzzy set. Soft Comput. 12(2), 165-175 (2008
5. Galichet, S., Dubois, D., Prade, H.: Imprecise specification of ill-known functions using gradual rules. International Journal of Approximate Reasoning 35, 205-222 (2004
6. Dubois, D., Prade, H.: Gradual inference rules in approximate reasoning. Information Sciences 61(1-2), 103-122 (1992
7. Jones, H., Dubois, D., Guillaume, S., Charnomordic, B.: A practical inference method with several implicative gradual rules and a fuzzy input: one and two dimensions. In: Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007, IEEE International, pp. 1-6 (2007
8. Bosc, P., Pivert, O., Ughetto, L.: On data summaries based on gradual rules. In: Proceedings of the 6th International Conference on Computational Intelligence, Theory and Applications, pp. 512-521. Springer, Heidelberg (1999
9. Hüllermeier, E.: Implication-based fuzzy association rules. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol.2168, pp. 241-252. Springer, Heidelberg (2001
10. Fiot, C., Masseglia, F., Laurent, A., Teisseire, M.: Gradual trends in fuzzy sequential patterns. In: 12th International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems (2008
11. Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: 20th International Conference on Very Large Data Bases (VLDB 1994), pp. 487-499 (1994
12. Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential pattern mining using a bitmap representation. In: KDD 2002: Proceedings of the eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 429-435. ACM, New York (2002