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

页码：1104 / 1108

页数：5

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