Fuzzy set generation for fuzzy association rule mining in large databases

Intelligent Engineering Systems Through Artificial Neural Networks

Volumn 12, Issue , 2002, Pages 435-440

  Shen, Judong ^a   Chang, Shing I ^a   Stanley Lee, E ^a  

^a KANSAS STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BOOLEAN ALGEBRA; DATABASE SYSTEMS; FUZZY SETS;

FUZZY MEMBERSHIP FUNCTIONS;

DATA MINING;

EID: 0036999711     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

1. Agrawal, R., and Srikant, R. 1994. Fast algorithms for mining association rules. In Proceedings of the 20th international conference on very large databases held in Santiago, Chile, September 12-15, 1994, 487-99. San Francisco, CA: Morgan Kaufmann.
2. Kuok, C., Fu, A., and Wong, M., 1998. Mining fuzzy association rules in databases. SIGMOD Record 27(1): 41-6.
3. Hong, Tzung-Pei, Kuo, Chan-Sheng, and Chi, Sheng-Chai. Mining Association Rules from Quantitative Data, The Eighth International Fuzzy Systems Association World Congress, 1999, p 1-34.
4. Medasani, Swarup, Kim, Jaeseok and Krishnapuram, Raghu. Overview of membership function generation techniques for pattern recognition, International Journal of Approximate Reasoning, v19, n34, Oct-Nov, 1998, p 391-417.
5. Han, Jiawei and Kamber, Micheline, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, August 2000.
6. Ray, Siddheswar and Turi, Rose H.. Determination of Number of Clusters in K-Means Clustering and Application in Colour Image Segmentation, Proceedings of the 4th international Conference on Advances in Pattern Recognition and Digital Techniques (ICAPRDT'99), Calcutta, India, 27-29 December, 1999.