Classification rule acquisition based on extended concept lattice

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

Volumn 4688 LNCS, Issue , 2007, Pages 571-578

  Wang, Yan ^a   Li, Ming ^a  

^a LANZHOU UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA MINING; MATHEMATICAL MODELS;

EXPERIMENT RESULTS; EXTENDED CONCEPT LATTICE;

LOGIC PROGRAMMING;

EID: 38049053503     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74769-7_61     Document Type: Conference Paper

References (17)

1. Wille, R.: Recon structing lattice theory: an approach based on hierarchies of concepts. In: Rival, I. (ed.) Ordered Sets, pp. 445-470. Reidel, Dordrecht-Boston (1982)
2. Godin, R.: Incremental concept formation algorithm based on Galois(concept) lattices. Computational Intelligence 11(2), 246-267 (1995)
3. Njiwoua, P., Nguifo, E.M.: Forwarding the choice of bias LEGAL-F:Using feature selection Toreduce the complexity of LEGAL. In: Walter, D. (ed.) Proceedings of the BENELEARN-97, ILKandINFOLAB, pp. 89-98. Tiburg Univer-sityPress, Netherlands (1997)
4. Carpineto, C., Romano, G.: Galois: anorder-heoretic approach to conceptual clustering. In: Utgoff, P. (ed.) Proceedings of the ICML 93, pp. 33-40. ElsevierSciencePublishers, Amhers(1993)
5. Oosthuizen, G.D.: The application of concept lattice to machine learning. Technical Report, University of Pretoria, South Africa (1996)
6. Sahami, M.: Learning classification rules using lattices. In: Lavran, N., Wrobel, S. (eds.) Proceedings Of the ECML-95, pp. 343-346. ElsevierSciencePublishers, Grete (1995)
7. Godin, R., Missaoui, R.: An incremental concept formation approach for learning from databases. Theoretical Computer Science 133, 387-419 (1994)
8. Fu, H.Y., Fu, H.G., Njiwoua, P., Nguifo, E.M.: A Comparative Study of FCA-Based Supervised Classification Algorithms. In: Proceedings of the Second International Conference on Formal Concept Analysis, Sydney,Australia, February 23-26, 2004, pp. 313-320 (2004)
9. Carpineto, C., Romano, G.: Galois: An order-theoretic approach to conceptual clustering. In: Proceedings of the Tenth International Conference on Machine Learning, University of Massachusetts, Amherst, MA, USA, June 27-29, 1993, pp. 33-40 (1993)
10. Sahami, M.: Learning Classification Rules Using Lattices. In: Proceedings of the Eighth European Conference on Machine Learning, Heraclion, Crete, Greece, pp. 343-346 (April 1995)
11. Mephu-Nguifo, E.: Galois Lattice:A Framework for Concept Learning.Design, Evaluation and Refinement. In: Proceedings of the Sixth International Conference on Tools with Artificial Intelligence, New Orleans, Louisiana, USA, November 6-9, 1994, pp. 461-467 (1994)
12. Xie, Zh.P., Liu, Z.T.: Research on Classifier Based on Lattice Structure. In: 16# World Computer congress 2000. Proceeding of conference on Intelligent Information Processing, Beijing, china, August 21-25, 2000, pp. 333-338 (2000)
13. Anamika, G., Naveen, K., Vasudha, B.: Incremental Classification Rules Based on Association Rules Using Formal Concept Analysis. In: Proceedings of the 4th International Conference on Machine Learning and Data Mining in Pattern Recognition, Leipzig, Germany, July 9-11, 2005, pp. 11-20 (2005)
14. Ganter, B., Wille, R.: Formal concept analysis. Springer, Heidelberg (1999)
15. Wang, G.Y., He, X.: A self-Learning Model under Uncertain Conditions. Journal of Software (in Chinese) 14, 1096-1102 (2003)
16. http://www.ics.uci.edy/~mlearn/MLRepository
17. Hou, L.J., Wang, G.Y., Nie, N., Wu, Y.: Discretization in rough set theory. Computer Science 27, 89-94 (2000).