A fuzzy attribute-oriented induction method for knowledge discovery in relational databases

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

Volumn 1552, Issue , 1999, Pages 1-13

  Mouaddib, Noureddine ^a   Raschia, Guillaume ^a  

^a UNIVERSITÉ DE NANTES   (France)

Author keywords

[No Author keywords available]

Indexed keywords

DATA WAREHOUSES; INFORMATION MANAGEMENT; THESAURI;

BACK-GROUND KNOWLEDGE; DOMAIN REPRESENTATIONS; FUZZY ATTRIBUTES; FUZZY THESAURI; GENERALIZATION PROCESS; LINGUISTIC VARIABLE; RELATIONAL DATABASE;

DATA MINING;

EID: 84947729527     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-49121-7_1     Document Type: Conference Paper

References (12)

1. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds): Advances in Knowledge Discovery and Data Mining. AAAI Press/ the MIT Press, ch. 1, pp. 137 (1996) 1
2. lHan, J., Cai, Y., Cercone, N.: Knowledge discovery in databases: An attribute-oriented approach. 18th Very Large Databases Conference, (Vancouver, Canada), pp. 547-559 (1992) 1, 12
3. Han, J., Fu, Y., Wang, W., Chiang, J., Gong, W., Koperski, K., Li, D., Lu, Y., Xia, A.: Dbminer: a system for mining knowledge in large relational databases. 2nd International conference on knowledge discovery in databases and data mining, (Portland, Usa), pp. 250-255 (1997) 1
4. Subtil, P., Mouaddib, N., Foucaut, O.: Fuzzy thesaurus for databases. 6th International Fuzzy Systems Association World Congress - Vol. 2, (Sao Paulo, Brazil), pp. 349-352, (1995) 2
5. Zadeh, L.: Concept of a linguistic variable and its application to approximate reasoning (1). Information Systems, Vol. 8, pp. 199-249 (1975) 4
6. Bosc, P., Dubois, D., Prade, H.: Approximate data reduction and fuzzy functionnal dependencies. 6th International Fuzzy Systems Association World Congress - Vol. 2, (Sao Paulo, Brazil), pp. 369-371, (1995) 4
7. Yager, R.: Linguistic summaries as a tool for database discovery. FUZZ-IEEE'95 Worskshop on Fuzzy Databases Systems, (Yokohama, Japan), pp. 79-84, (1995) 12
8. Michalsky, R.S., Stepp, R.: Learning from observation: conceptual clustering. Michalsky, R.S., Carbonell, J.G. and Mitchell T.M. eds, Machine Learning: an Artificial Intelligence Approach, Vol. 1, ch. 11, pp. 331-363 (1983) 4
9. Quinlan, J.R.: Induction of decision trees. Machine Learning, pp. 81-106 (1986) 5
10. Fisher, D.H.: Knowledge acquisition via incremental conceptual clustering. Machine Learning, Vol. 2, pp. 139-172 (1987) 5, 12
11. Gennari, J.H., Langley, P., Fisher, D.H.: Models of incremental concept formation. In Knowledge Acquisition and Learning, Buchanan, B.G. and Wilkins, D.C. (eds) (1989) 5
12. Kodratoff, Y., Diday, E.: Induction symbolique et numerique a partir des domnšes. Cepadues-Editions (1991) 4