Enhancing the Efficiency in Mining Weighted Frequent Itemsets

被引:4
|
作者
Lan, Guo-Cheng [1 ]
Hong, Tzung-Pei [2 ]
Lee, Hong Yu [2 ]
Wang, Shyue-Liang [3 ]
Tsai, Chun-Wei [4 ]
机构
[1] Natl Cheng Kung Univ, Dept Comp Sci & Informat Engn, Tainan 701, Taiwan
[2] Natl Univ Kaohsiung, Dept Comp Sci & Informat Engn, Kaohsiung, Taiwan
[3] Natl Univ Kaohsiung, Dept Informat Management, Kaohsiung, Taiwan
[4] Chianan Univ Pharmacy & Sci, Dept Informat Technol, Tainan, Taiwan
来源
2013 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2013) | 2013年
关键词
Data mining; weighted data mining; weighted frequent itemset mining; upper-bound model;
D O I
10.1109/SMC.2013.192
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
To further enhance the performance of finding weighted frequent itemsets, 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.
引用
收藏
页码:1104 / 1108
页数:5
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