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2010 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (2007)
Haier International Training Center, Qingdao, China
July 30, 2007 to Aug. 1, 2007
ISBN: 0-7695-2909-7
pp: 803-808
Zhang Hong , China University of Mining and Technology, China
Cai Zheng-Xing , China University of Mining and Technology, China
Kong Ling-Dong , China University of Mining and Technology, China
Zhang Bo , China University of Mining and Technology, China
ABSTRACT
This paper defined a kind of multi-dimension data cube model, and presented a new formalization of generalized association rule based on data cube model. After comprehending the weaknesses of the current generalized association rule mining algorithms based on data cube, we proposed a new algorithm GenHibFreq which was suitable for mining multi-level frequent itemset based on data cube. By taking advantage of the item taxonomy, algorithm GenHibFreq reduced the number of candidate itemsets counted, and had better efficiency. We also designed an algorithm GenerateLHSs-Rule for generating generalized association rule from multi-level frequent itemset. Demonstrated through examples, algorithms proposed in this paper had better efficiency and less generated redundant rules than several existing mining algorithms, such as Cumulate, Stratify and ML_T2L1, and had good performance in flexibility, scalability and complexity and had new ideas on conducting the Generalized Association Rule Mining Algorithms in multi-dimension enviornment nad it also has great theroritical meaning and practical value..
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CITATION
Zhang Hong, Cai Zheng-Xing, Kong Ling-Dong, Zhang Bo, "Generalized Association Rule Mining Algorithms based on Data Cube", 2010 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, vol. 02, no. , pp. 803-808, 2007, doi:10.1109/SNPD.2007.291
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