A definition for fuzzy approximate dependencies

Fuzzy Sets and Systems

Volumn 149, Issue 1, 2005, Pages 105-129

  Berzal, F ^a   Blanco, I ^b   Sanchez D ^a   Serrano, J M ^c   Vila, M A ^a  

^a UNIVERSITY OF GRANADA   (Spain)

^b UNIVERSITY OF ALMERÍA   (Spain)

^c UNIVERSITY OF JAÉN   (Spain)

Author keywords

Data mining; Fuzzy approximate dependencies; Fuzzy association rules; Fuzzy relational databases

Indexed keywords

ALGORITHMS; DATA MINING; DATA PROCESSING; DATABASE SYSTEMS; MATHEMATICAL MODELS; SET THEORY;

FUZZY APPROXIMATE DEPENDENCIES; FUZZY ASSOCIATION RULES; FUZZY RELATIONAL DATABASES; FUZZY SUBSETS;

FUZZY SETS;

EID: 9644287892     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fss.2004.07.012     Document Type: Article

References (65)

1. R. Agrawal, T. Imielinski, A. Swami, Mining association rules between sets of items in large databases, in: Proc. ACM SIGMOD Conf. on Management of Data, Washington, D.C., May,1993, pp. 207-216.
2. R. Agrawal, R. Srikant, Fast algorithms for mining association rules, in: Proc. 20th Internat. Conf. on Very Large Databases, Santiago, Chile,September 1994, pp. 478-499.
3. J. Alias, Mapa de suelos de Mula, Mapa 1:100000 y memoria, LUCDEME; MAPA-ICONA-University of Murcia, 1986.
4. J. Alias, Mapa de suelos de Cehegin, Mapa 1:100000 y memoria, LUCDEME; MAPA-ICONA, University of Murcia, 1987.
5. W.H. Au, K.C.C. Chan, An effective algorithm for discovering fuzzy rules in relational databases, in: Proc. IEEE Internat. Conf. on Fuzzy Systems, Vol. II, 1998, pp. 1314-1319.
6. F. Berzal, I. Blanco, D. Sánchez, M.A. Vila, A new framework to assess association rules, in: F. Hoffmann (Ed.), Advances in Intelligent DataAnalysis, Fourth International Symposium, IDA'01, Lecture Notes in Computer Science 2189, Springer, Berlin, 2001, pp. 95-104.
7. F. Berzal, I. Blanco, D. Sánchez, M. Vila, Measuring the accuracy and interest of association rules: a new framework, Intelligent Data Analysis 6 (2002) 221-235.
8. J.C. Bezdek, J.D. Harris, Fuzzy partitions and relations: an axiomatic basis for clustering, Fuzzy Sets and Systems 1, North-Holland, Amsterdam, 1978, pp. 111-127.
9. J.M. Bigham, E.J. Ciolkosz, Eds. Soil color. Soil Sci. Soc. Am. Spec. Publ. No. 31, (1993) 159.
10. I. Blanco, M.J. Martín-Bautista, D. Sánchez, M.A. Vila, On the support of dependencies in relational databases: strong approximate dependencies. Data Mining and Knowledge Discovery, submitted.
11. P. Bosc, L. Lietard, O. Pivert, Functional dependencies revisited under graduality and imprecision, 1997 Annual Meeting of NAFIPS, 1997, pp. 57-62.
12. P. Bosc, D. Dubois, O. Pivert, H. Prade, On fuzzy association rules based on fuzzy cardinalities, FUZZ-IEEE (2001) 461-464.
13. P.D. Bra, J. Paredaens, Horizontal decompositions for handling exceptions to functional dependencies, Adv. Database Theory 2 (1983) 123-144.
14. K.C.C. Chan, W.-H. Au, Mining Fuzzy Association Rules, CIKM (1997) 209-215.
15. G. Chen, Q. Wei, Fuzzy association rules and the extended mining algorithms, Inform. Sci. 147 (1-4) (2002) 201-228.
16. J.C. Cubero, M.A. Vila, A new definition of fuzzy functional dependency in fuzzy relational databases, Int. J. Intelligent Systems 9 (5) (1994) 441-448.
17. J.C. Cubero, O. Pons, M.A. Vila, Weak and strong resemblances in fuzzy functional dependencies, in: Proc. IEEE Internat. Conf. on Fuzzy Systems, Orlando/FL, USA, 1994, pp. 162-166.
18. J.C. Cubero, J.M. Medina, O. Pons, M.A. Vila. The generalized selection: an alternative way for the quotient operations in fuzzy relational databases, in: B. Bouchon-Meunier, B. Yager, R.R. Zadeh, L.A. (Eds.), Fuzzy Logic and Soft Computing, Vol. 5, World Scientific, USA, 1995, 5, pp. 214-250.
19. J.C. Cubero, F. Cuenca, I. Blanco, M. A. Vila, Incomplete functional dependencies versus knowledge discovery in databases, in: Proc. EUFIT'98, Aachen, Germany, 1998, pp. 731-774.
20. J.M. de Graaf, W.A. Kosters, J.J.W. Witteman (2001). Interesting Fuzzy Association Rules in Quantitative Databases, PKDD (2001) 140-151.
21. G. Delgado, et al., Mapa de Suelos de Vera, LUCDEME, ICONA-Universidad de Granada, 1991.
22. M. Delgado, D. Sánchez, M.A. Vila, Fuzzy quantified dependencies in relational databases, in: Proc. EUFIT'99, Aachen, Germany, 1999.
23. M. Delgado, M.J. Martín-Bautista, D. Sánchez, M.A. Vila, Mining strong approximate dependencies from relational databases, in: Proc. IPMU'2000, Vol. 2, Madrid, Spain, 2000a, pp. 1123-1130.
24. M. Delgado, D. Sánchez, M.A. Vila, Fuzzy cardinality based evaluation of quantified sentences, Internat. J. Approx. Reason. 23 (2000) 23-66.
25. M. Delgado, D. Sánchez, M.A. Vila, Fuzzy cardinality based evaluation of quantified sentences, Internat. J. Approx. Reason. 23 (2000b) 23-66.
26. M. Delgado, D. Sánchez, J.M. Serrano, M.A. Vila, A survey of methods to evaluate quantified sentences, Mathware and Soft Comput. VII(2-3) (2000) 149-158.
27. M. Delgado, N. Marín, D. Sánchez, M. A. Vila, Fuzzy association rules: general model and applications, IEEE Trans. Fuzzy Systems 11 (2) (2003) 214-225.
28. M. Delgado, N. Marín, D. Sánchez, M.A. Vila, Mining fuzzy association rules: an overview. 2003 BISC International Workshop on Soft Computing for Internet and Bioinformatics, 2003, accepted.
29. D. Dubois, E. Hüllermeier, H. Prade, A note on quality measures for fuzzy association rules. Fuzzy Sets and Systems - IFSA 2003, Lecture Notes in Artificial Intelligence, Vol. 2715, Springer, Berlin, pp. 346-353.
30. J. Dunn, A graph theoretic analysis of pattern classification via Tamura's fuzzy relation, IEEE Trans. SMC-4 3 (1974) 310-313.
31. A. Gyenesei, Interestingness measures for fuzzy association rules, in: Proc. PKDD,2001, pp. 152-164.
32. T.P. Hong, C.S. Kuo, S.C. Chi, Mining association rules from quantitative data, Intelligent Data Analysis 3 (1999) 363-376.
33. Y. Huhtala, J. Karkkainen, P. Porkka, H. Toivonen, Efficient discovery of functional and approximate dependencies using partitions, in: Proc. 4th Internat. Conf. on Data Engineering, 1998, pp. 392-401.
34. E. Hüllermeier, Implication-Based Fuzzy Association Rules, in: Proc. PKDD,2001, pp. 241-252.
35. F. Hussain, H. Liu, C.L. Tan, M. Dash, Discretization: an enabling technique, Technical Report, The National University of Singapore, June 1999.
36. A. Kandel, L. Yelowitz, Fuzzy chains, IEEE Trans. SMC-4 5 (1974) 472-475.
37. M. Kaya, R. Alhajj, F. Polat, A. Arslan, Efficient Automated Mining of Fuzzy Association Rules, DEXA (2002) 133-142.
38. J. Kivinen, H. Mannila, Approximate dependency inference from relations, Theoretical Computer Science 149 (1) (1995) 129-149.
39. C.-M. Kuok, A. Fu, M.H. Wong, Mining fuzzy association rules in databases, SIGMOD Record 27 (1) (1998) 41-46.
40. J.H. Lee, H.L. Kwang, An extension of association rules using fuzzy sets, in: Proc. IFSA'97, Prague, Czech Republic, 1997.
41. J. Luo, S. Bridges, Mining fuzzy association rules and fuzzy frequency episodes for intrusion detection, Internat. J. Intelligent Systems 15 (8) (2000) 687-703.
42. A. Munsell, Soil Color Charts, Munsell Color Company Inc., Baltimore, Maryland, 1954.
43. C. Oyonarte, Estudio Edáfico de la Sierra de Gádor (Almería), Evaluación para usos forestales, Ph.D. Thesis, University of Granada, 1999.
44. W. Pedrycz, Fuzzy Set Technology in Knowledge Discovery, Fuzzy Sets and Systems 98 (1998) 279-290.
45. A. Pérez-Pujalte, Mapa de suelos de Tabernas, Mapa 1:100000 y memoria, LUCDEME; MAPAICONA-CSIC, 1987.
46. A. Pérez-Pujalte, Mapa de suelos de Sorbas, Mapa 1:100000 y memoria, LUCDEME; MAPA-ICONA-CSIC, 1989.
47. B. Pfahringer, S. Kramer, Compression-based evaluation of partial determinations, in: Proc. First Internat. Conf. Knowledge Discovery and Data Mining (KDD'95),1995, pp. 234-239.
48. D. Sánchez, Adquisición de relaciones entre atributos en bases de datos relacionales, Ph.D. Thesis (in Spanish), University of Granada, 1999.
49. D. Sánchez, J.M. Serrano, M.A. Vila, V. Aranda, J. Calero, G. Delgado, Using Data Mining Techniques to Analyze Correspondences Between User and Scientific Knowledge in an Agricultural Environment, in: M. Piattini, J. Filipe, J. Braz (Eds.), Enterprise Information Systems IV, Kluwer Academic Publishers, Dordrecht, MA, 2003, pp. 75-89.
50. M. Sánchcz-Marañón, Los suelos del Macizo de Sierra Nevada, Evaluación y capacidad de uso (in Spanish), Ph.D. Thesis, University of Granada, 1992.
51. M. Sánchez-Marañón, G. Delgado, M. Melgosa, E. Hita, R. Delgado, CIELAB color parameters and their relationship to soil characteristic in Mediterranean Red Soils, Soil Sci. 11 (1997) 833-842.
52. U. Schwertmann, Relations between iron oxides, soil color and soil formation, Soil Sci. Soc. Am. Spec. Publ. No 31 (1993) 51-69.
53. D.G. Schulze, J.L. Nagel, G.E. Van Scoyoc, T.L. Henderson, M.F. Baumgardner, D.E. Scott, Significance of organic matter in determining soil colors, Soil Sci. Soc. Am. Spec. Publ. N? 31 (1993) 71-90.
54. S. Shenoi, A. Melton, Proximity relations in the fuzzy relational database model, Fuzzy Sets and Systems 31 (1989) 285-296.
55. E. Shortliffe, B. Buchanan, A model of inexact reasoning in medicine, Math. Biosci. 23 (1975) 351-379.
56. Soil Survey Staff Soil Taxonomy, U.S. Dept. Agri. Handbook No. 436, 754pp.
57. S. Tamura, S. Higuchi, K. Tanaka, Pattern classification based on fuzzy relations, IEEE Trans. SMC-1 (1971) 61-66.
58. J. Torrent, U. Schwertmann, D.G. Schulze, Iron oxide mineralogy of two river terraces sequences in Spain, Geoderma 23 (1980) 191-208.
59. S.-L. Wang, J.-S. Tsai, Discovery of approximate dependencies from proximity-based fuzzy databases, in: Proc. 3rd Internat. Conf. on Knowledge-based Intelligent Information Engineering Systems, August 1999, Adelaide, Australia, 1999, pp. 234-237.
60. S.-L. Wang, J.-S. Tsai, B.-C. Chien, Mining Approximate Dependencies Using Partitions on Similarity-relation-based Fuzzy Databases, in: Proc. IEEE SMC'99, October 1999, Tokyo, Japan, V-871-V-875,1999.
61. S.-L. Wang, J.-S. Tsai, T.-P. Hong, Mining functional dependencies from fuzzy relational databases, in: Proc. ACM SAC 2000, Fuzzy Application and Soft Computing Track, March 2000, Italy,2000, pp. 490-493.
62. H.-T. Yang, S.-J. Lee, Mining Fuzzy Association Rules from Sequence databases with Quantitative data and Inter-Transaction Intervals, FSKD (2002) 606-610.
63. Y. Yang, M. Singhal, Fuzzy functional dependencies and fuzzy association rules, DaWaK (1999) 229-240.
64. L.A. Zadeh, Similarity relations and fuzzy orderings, Inform. Sci. 3 (1971) 177-200.
65. L.A. Zadeh, A computational approach to fuzzy quantifiers in natural languages, Comput. Math. Appl. 9 (1) (1983) 149-184.