Association Rule Mining Based on the Semantic Categories of Tourism Information

被引：0
|
作者：
Zhou, Yipeng [1 ,2 ]
Du, Junping [3 ]
Zeng, Guangping [1 ]
Tu, Xuyan [1 ]
机构：
[1] Univ Sci & Technol Beijing, Informat Engn Sch, Beijing 100083, Peoples R China
[2] Beijing Technol & Business Univ, Sch Comp Sci, Beijing 100037, Peoples R China
[3] Beijing Univ Posts & Telecommun, Sch Comp Sci & Technol, Beijing Key Lab Intelligent Telecommun Software &, Beijing 100876, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2008, PT 2, PROCEEDINGS | 2008年 / 5264卷
基金：
中国国家自然科学基金; 北京市自然科学基金;
关键词：
Association rule; tourism emergency; genetic algorithm; text mining;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
It is difficult for traditional data mining algorithms to mine semantic information from text set because of its complexity and high dimension. To solve this problem, the semantic categories of words appearing in tourism emergency reports are studied, and a semantic association rule training algorithm is presented based on these categories. Association words are also gained from these rules, which can better describe the semantic contents of the texts. Quantum-inspired genetic algorithm is utilized to improve the effectiveness of rule-searching process. Experiments show the better results than traditional methods.
引用
收藏
页码：67 / +
页数：3
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