Multidimensional fuzzy partitioning of attribute ranges for mining quantitative data

International Journal of Intelligent Systems

Volumn 19, Issue 11, 2004, Pages 1111-1126

  Gyenesei, Attila ^a   Teuhola, Jukka ^a  

^a UNIVERSITY OF TURKU   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

CUSTOMER SATISFACTION; DATA ACQUISITION; DATA MINING; DECISION MAKING; DECISION SUPPORT SYSTEMS; GENETIC ALGORITHMS; STATISTICAL METHODS;

DATA DISTRIBUTIONS; FUZZY ASSOCIATION RULES; FUZZY PATTERNS; MULTIVARIATE MERGING TECHNIQUES;

FUZZY SETS;

EID: 7544230450     PISSN: 08848173     EISSN: None     Source Type: Journal    
DOI: 10.1002/int.20039     Document Type: Article

References (29)

1. Agrawal R, Imielinski T, S wami A. Mining association rules between sets of items in large databases. In: Buneman P, Jajodia S, editors. Proc ACM SIGMOD. New York: ACM Press; 1993. pp 207-216.
2. Agrawal R, Srikant R. Fast algorithm for mining association rules in large databases. In: Bocca JB, Jarke M, Zaniolo C, editors. Proc 20th VLDB Conf. San Francisco, CA: Morgan Kaufmann; 1994. pp 487-499.
3. Srikant R, Agrawal R. Mining quantitative association rules in large relation tables. In: Widom J, editor. Proc ACM SIGMOD. New York: ACM Press; 1996. pp 1-12.
4. Kuok CM, Fu A, Wong MH. Fuzzy association rules in databases. ACM SIGMOD Rec 1998;27:41-46.
5. Zadeh LA. Fuzzy sets. Inform Control 1965;8:338-353.
6. Au WH, Chan KCC. An effective algorithm for discovering fuzzy rules in relational databases. In: Proc. 1998 IEEE Int Conf on Fuzzy Systems. Los Alamitos, CA: IEEE Computer Society; 1998; pp 1314-1319.
7. Chan KCC, Au WH. Mining fuzzy association rules. In: Golshani F, Makki K, editors. Proc 6th ACM Int Conf on Information and Knowledge Management. New York: ACM Press; 1997. pp 209-215.
8. Chen G, Wei Q. Fuzzy association rules and the extended mining algorithms. Inform Sci 2002;147:201-228.
9. Chen G, Wei Q, Kerre E. Fuzzy data mining: Discovery of fuzzy generalized association rules. In: Bordogna G, Pasi G, editors, Recent issues on fuzzy databases. Heidelberg: Physica-Verlag; 2000. pp 45-66.
10. Gyenesei A, Teuhola J. Mining fuzzy frequent patterns without repeated candidate generation. In: Wang L. Halgamuge S, Yao X, editors. Proc 1st Int Conf on Fuzzy Systems and Knowledge Discovery. Singapore: Nanyang Technological University; 2002. pp 374-380.
11. Hong TP, Kuo CS, Chi SC. Mining association rules from quantitative data. Intell Data Anal 1999;3:363-376.
12. Mazlack LJ. Granulation of quantitative association rules. Int J Fuzzy Syst 2001;3:400-408.
13. Zhang W. Mining fuzzy quantitative association rules. In: IEEE Int Conf on Tools and Artificial Intelligence. Los Alamitos, CA: IEEE Computer Society; 1999. pp 99-102.
14. Jain AK, Murty MN, Flynn PJ. Data clustering: A review. ACM Comput Surv 1999;31: 264-323.
15. Wang K, Tay SHW, Liu B. Interestingness-based interval merger for numeric association rules. In: Agrawal R, Stolorz P, Piatetsky-Shapiro G, editors. Proc 4th Int Conf on Knowledge Discovery and Data Mining. Menlo Park, CA: AAAI; 1998. pp 121-147.
16. Bay SD. Multivariate discretization for set mining. Knowl Inform Syst 2001;3:491-512.
17. Wang K, Liu B. Concurrent discretization of multiple attributes, in: Lee H-Y, Motoda HM, editors. Proc 5th Pacific Rim Int Conf on Artificial Intelligence. London: Springer-Verlag; 1998. pp 250-259.
18. Nievergelt J, Hinterberger H. The grid file: An adaptable, symmetric multikey file structure. ACM Trans. Database Syst 1984;9:38-71.
19. Gyenesei A. Determining fuzzy sets for quantitative attributes in data mining problems. In: Mastorakis N, editor. Advances in Fuzzy Systems and Evolutionary Computation. New York and Athens: World Scientific Engineering Society Press. 2001; pp 48-53.
20. Han J, Kamber M. Data mining: Concepts and techniques. San Francisco, CA: Morgan Kaufmann; 2001.
21. Cormen TH, Leiserson CE, Rivest RL. Introduction to algorithms. Cambridge, MA: MTT Press; 1990.
22. Bentley JL. Multidimensional binary search trees in database applications. TFKK Trans on Softw Eng 1979;5:339-353.
23. Carlsson C, Fullér R. Fuzzy reasoning in decision making and optimization. New York: Physica-Verlag; 2002.
24. Hilderman RJ, Hamilton HJ. Knowledge discovery and interestingness measures: A survey. Technical Report CS 99-04, University of Regina, Canada, 1999.
25. Gyenesei A, Teuhola J. Interestingness measures for fuzzy association rules. In: De Raedt L, Siebes A, editors. Principles of data mining and knowledge discovery. New York: Springer; 2001. pp 152-164.
26. U.S. Bureau of the Census, http://staff.cs.utu.fi/~gyenesei/datasets/.
27. Nash WJ, SeEers TL, Talbot SR, Cawthom AJ, Ford WB. The population biology of abalone in Tasmania. Technical Report No. 48, Sea Fisheries Division, 1994.
28. Principles and Practice of Knowledge Discovery in Databases (PKDD'99) Conference website, http://lisp.vse.cz/pkdd99/; 1999.
29. MacQueen J. Some methods for classification and analysis of multivariate observations. In: LeCam LM, Neyman, J, editors. 5th Berkeley Symposium on Mathematics, Statistics and Probability. Berkeley, CA: University of California Press; 1967. pp 281-296.