Integration of two fuzzy data mining methods

Neural Network World

Volumn 14, Issue 5, 2004, Pages 391-402

  Horvath T ^a   Krajci S ^a  

^a P J AFÁRIK UNIVERSITY   (Slovakia)

Author keywords

Clustering; Concept lattices; Fuzzy and annotated programs; Fuzzy data; Graded classification; Inductive logic programming

Indexed keywords

CLUSTERING; CONCEPT LATTICES; FUZZY AND ANNOTATED PROGRAMS; FUZZY DATA; GRADED CLASSIFICATION; INDUCTIVE LOGIC PROGRAMMING;

DATA ACQUISITION; FUZZY SETS; HIERARCHICAL SYSTEMS; IDENTIFICATION (CONTROL SYSTEMS); LOGIC PROGRAMMING; OBJECT RECOGNITION;

DATA MINING;

EID: 9144245114     PISSN: 12100552     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (17)

1. Bělohlávek R.: Concept Lattices and Order in Fuzzy Logic, Annals of Pure and Applied Logic, 128, 2004, pp. 277-298.
2. Bělohlávek R.: Algorithms for fuzzy concept lattices. Proc. 4th Conference on Recent Advances in Soft Computing. Nottingham, United Kingdom, 2002, ISBN 1-84233-0764, pp. 67-68 (extended abstract); pp. 200-205 (full paper on the included CD).
3. Bělohlávek R., Sklenář V., Zacpal J.: Crisply generated fuzzy concepts: reducing the number of concepts in formal concept analysis (submitted to RASC 2004).
4. Džeroski S., Lavrač N.: An introduction to inductive logic programming. Relational data mining, S, Džeroski, N. Lavrač eds. Springer, 2001, pp. 48-73.
5. Ganter B., Wille R.: Formal Concept Analysis, Mathematical Foundation, Springer Verlag, 1999.
6. Horváth T., Vojtáš P.: Efficient induction for graded classification. Accepted as short paper to ILP'04, Porto, Portugal, 2004.
7. Kifer M., Subrahmanian V. S.: Theory of generalized annotated logic programming and its applications. J. Logic Programming, 12, 1992, pp. 335-367.
8. Krajči S.: Cluster based efficient generation of fuzzy concepts. In: Workshop on Soft Computing, Neural Network World, ICS, 13, 2003, pp. 521-530.
9. Pollandt S.: Fuzzy Begriffe, Springer-Verlag, Berlin/Heidelberg, 1997.
10. Prade H., Richard G., Serrurier M.: Enriching relational learning with fuzzy predicates. In: Proc. PKDD'03, LNCS 2838, Springer, 2003, pp. 399-410.
11. Rice M. D., Siff M.: Clusters, Concepts and Pseudometrics, Proc. Math. Foundations of Computer Science and Information Technology, Electr. Notes in Theor. Comp. Sci., Elsevier, 2001.
12. Shibata D. et al: An induction algorithm based on fuzzy logic programming. In Proc. PAKDD'99, LNCS 1574, Springer, 1999, pp. 268-273.
13. A. Srinavasan. The Aleph Manual. Tech. Rep. Comp. Lab. Oxford Univ. 2000 at http://web.comlab.ox.ac.uk/oucl/research/areas/machlearn/Aleph/aleph.html
14. Sudzina F.: Trends in management information systems. In: IMEA 2001. Pardubice: Faculty of Economics and Administration, University of Pardubice, 2001, ISBN 80-7194-347-9, pp. 314-318.
15. Vojtáš P.: Fuzzy logic programming. Fuzzy sets and systems, 124, 2001, pp. 361-370.
16. Vojtáš P., Horváth T., Krajči S., Lencses R.: An ILP model for a monotone graded classification problem. Kybernetika, 40, 2004, pp. 317-332.
17. Yahia S., Jaoua A.: Discovering knowledge from fuzzy concept lattice. In: Kandel A., Last M., Bunke H.: Data Mining and Computational Intelligence, Physica-Verlag, 2001, pp. 169-190.