DocumentCode :
3038765
Title :
Enhancing the Efficiency in Mining Weighted Frequent Itemsets
Author :
Guo-Cheng Lan ; Tzung-Pei Hong ; Hong Yu Lee ; Shyue-liang Wang ; Chun-Wei Tsai
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan
fYear :
2013
fDate :
13-16 Oct. 2013
Firstpage :
1104
Lastpage :
1108
Abstract :
To further enhance the performance of finding weighted frequent item sets, this work presents an effective upper-bound model for reducing unpromising candidates in mining process. To achieve this goal, a projection-based pruning strategy based on our previously proposed model is developed to gradually tighten the upper-bound value for each transaction. The experimental results show that the proposed approach can achieve good performance in efficiency.
Keywords :
data mining; data mining process; projection-based pruning strategy; upper-bound model; weighted frequent itemsets; Algorithm design and analysis; Conferences; Data mining; Educational institutions; Itemsets; Data mining; upper-bound model; weighted data mining; weighted frequent itemset mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on
Conference_Location :
Manchester
Type :
conf
DOI :
10.1109/SMC.2013.192
Filename :
6721945
Link To Document :
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