Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) (2006)
Hong Kong, China
Dec. 18, 2006 to Dec. 22, 2006
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2006.16
Zahra Farzanyar , Iran University of Science and Technology, Tehran
Mohammadreza kangavari , Iran University of Science and Technology, Tehran
Sattar Hashemi , Iran University of Science and Technology, Tehran
Association rule mining is an active data mining research area. Recent years have witnessed many efforts on discovering fuzzy associations. The key strength of fuzzy association rule mining is its completeness. This strength, however, comes with a major drawback to handle large datasets. It open produces a huge number of candidate itemsets. The huge number of candidate itemsets makes it ineffective for a data mining system to analyze them. To overcome this problem in this study, fuzzy association rule mining system is driven by domain ontology. It describes the use of a concept hierarchy for improving the results of fuzzy association rule mining. Our ontology-based data mining algorithm makes the rules more visual, more interesting and more understandable. At last the paper, the efficiency and advantages of this algorithm has been approved by experimental results.
S. Hashemi, Z. Farzanyar and M. kangavari, "A New Algorithm for Mining Fuzzy Association Rules in the Large Databases Based on Ontology," Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)(ICDMW), Hong Kong, China, 2006, pp. 65-69.