Mining pure linguistic associations from numerical data

International Journal of Approximate Reasoning

Volumn 48, Issue 1, 2008, Pages 4-22

  Novák, Vilém ^a   Perfilieva, Irina ^a   Dvořák, Antonín ^a   Chen, Guoqing ^b   Wei, Qiang ^b   Yan, Peng ^b  

^a UNIVERSITY OF OSTRAVA   (Czech Republic)

^b TSINGHUA UNIVERSITY   (China)

Author keywords

Association rules; Data mining; Evaluative linguistic expressions; GUHA method; Linguistic associations

Indexed keywords

ASSOCIATION RULES; DATA MINING; EXPERT SYSTEMS; NATURAL LANGUAGE PROCESSING SYSTEMS; NUMERICAL METHODS;

LINGUISTIC ASSOCIATIONS; LINGUISTIC EXPRESSIONS; REAL-VALUED DATA;

LINGUISTICS;

EID: 40049106001     PISSN: 0888613X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijar.2007.06.005     Document Type: Article

References (33)

1. R. Agrawal, T. Imielinski, A. Swarmi, Mining association rules between sets of items in large databases, in: Proceedings of the ACM SIGMOD International Conference on Management of Data, Washington, DC, USA, 1993, pp. 207-216.
2. Agrawal R., Mannila H., Srikant R., Toivonen H., and Verkamo A.I. Fast discovery of association rules. In: Fayyad U., Piatetsky-Shapiro G., Smyth P., and Uthurusamy R. (Eds). Advances in Knowledge Discovery and Data Mining (1996), AAAI Press/The MIT Press, MA, USA 1-30
3. Agrawal R., and Shafer J. Parallel mining of association rules. IEEE Transactions On Knowledge And Data Engineering 8 (1996) 962-969
4. Carnap R. Meaning and Necessity: A Study in Semantics and Modal Logic (1947), University of Chicago Press, Chicago
5. Chen G.Q., Wei Q., and Kerre E.E. Fuzzy logic in discovering association rules: an overview. In: Triantaphyllou E., and Felici G. (Eds). Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques (2006), Springer, NY, USA 459-493
6. Chen G.Q., Wei Q., Liu D., and Wets G. Simple association rules (sar) and the sar-based rule discovery. Journal of Computer & Industrial Engineering 43 (2002) 721-733
7. Fayyad U., Piatetsky-Shapiro G., and Smyth P. From data mining to knowledge discovery: an overview. In: Fayyad U., Piatetsky-Shapiro G., Smyth P., and Uthurusamy R. (Eds). Advances in Knowledge Discovery and Data Mining (1996), AAAI Press/The MIT Press, MA, USA 1-30
8. T. Fukuda, Y. Morimoto, S. Morishita, Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization, in: Proceedings of the ACM SIGMOD International Conference on Management of Data, Montreal, Canada, 1996, pp. 12-13.
9. Hájek P. The question of a general concept of the guha method. Kybernetika 4 (1968) 505-515
10. Hájek P. Logics for data mining (guha rediviva). Neural Network World 10 (2000) 301-311
11. Hájek P., and Havránek T. Mechanizing Hypothesis Formation (Mathematical Foundations for a General Theory) (1978), Springer-Verlag, Berlin/Heidelberg/New York
12. Hájek P., and Novák V. The sorites paradox and fuzzy logic. International Journal of General Systems 32 (2003) 373-383
13. Harrison D., and Rubinfeld D. Hedonic housing prices and the demand for clean air. Journal of Environmental Economics and Management 5 (1978) 81-102
14. H. Mannila, H. Toivonen, A. Verkamo, Efficient algorithms for discovering association rules, in: AAAI Workshop on Knowledge Discovery in Databases, Seattle, Washington, 1994, pp. 181-192.
15. P. Materna, Concepts and Objects, Acta Philosophica Fennica 63, Helsinki, 1998.
16. Miller R.J., and Yang Y. Association rules over interval data. ACM SIGMOD 26 (1997) 452-461
17. Mitra S., Pal S.K., and Mitra P. Data mining in soft computing framework: a survey. IEEE Transactions on Neural Networks 13 1 (2002) 3-14
18. Novák V. Fuzzy Sets and Their Applications (1989), Adam Hilger, Bristol
19. Novák V. Towards formalized integrated theory of fuzzy logic. In: Bien Z., and Min K. (Eds). Fuzzy Logic and Its Applications to Engineering. Information Sciences, and Intelligent Systems (1995), Kluwer, Dordrecht 353-363
20. Novák V. Fuzzy logic deduction with words applied to ancient sea level estimation. In: Demicco R., and Klir G. (Eds). Fuzzy Logic in Geology (2003), Academic Press, Amsterdam 301-336
21. Novák V. On fuzzy type theory. Fuzzy Sets and Systems 149 (2005) 235-273
22. V. Novák, Fuzzy logic theory of evaluating expressions and comparative quantifiers, in: Proceedingd of 11th International Conference IPMU, Paris, July 2006, vol. 2, Éditions EDK, Les Cordeliers, Paris, 2006, pp. 1572-1579.
23. V. Novák, A comprehensive theory of trichotomous evaluative linguistic expressions, in: S. Gottwald, H.P., U. Höhle, E.P. Klement (Eds.), Proceedings of 26th Linz Seminar on Fuzzy Set Theory, Research Report No. 71, http://irafm.osu.cz/, in press.
24. Novák V., Perfilieva I., and Močkoř J. Mathematical Principles of Fuzzy Logic (1999), Kluwer, Boston
25. Piatecky-Shapiro G., and Frawley W.J. Knowledge Discovery in Databases (1991), AAAI Press/MIT Press, Cambridge, MA
26. R. Rastogi, K. Shim, Mining optimized association rules with categorical and numerical attributes, in: Proceedings of the l4th International Conference on Data Engineering, Orlando, FL, USA, 1998, pp. 503-512.
27. A. Savasere, E. Omiecinski, S. Navathe, An efficient algorithm for mining association rules in large databases, in: Proceedings of the 21th International Conference on Very Large Data Bases, Zurich, Switzerland, 1995, pp. 432-444.
28. R. Srikant, R. Agrawal, Mining quantitative association rules in large relational tables, in: Proceedings of the ACM SIGMOD International Conference on Management of Data, Montreal, Canada, 1996, pp. 1-12.
29. R. Srikant, Q. Vu, R. Agrawal, Mining association rules with item constraints, in: Proceedings of the Third International Conference on Knowledge Discovery in Databases and Data Mining, Newport Beach, California, USA, 1997.
30. Tseng S. Mining association rules with interestingness constraints in large databases. International Journal of Fuzzy Systems 3 (2001) 449-468
31. Q. Wei, G. Chen, Mining generalized association rules with fuzzy taxonomic structures, in: Proceedings of the 18th International Confernce NAFIPS'99, New York, NY, USA, 1999, pp. 477-481.
32. Yilmaz E., Triantaphyllou E., Chen J., and Liao T.W. A heuristic for mining association rules in polynomial time. Mathematical and Computer Modelling 37 (2003) 219-233
33. Zadeh L.A. The concept of a linguistic variable and its application to approximate reasoning I, II, III. Information Sciences 8-9 (1975) 199-257 pp. 301-357, 43-80