Association Rule Mining Based on the Semantic Categories of Tourism Information

Authors:
Yipeng Zhou;Junping Du;Guangping Zeng;Xuyan Tu
Affiliations:
Information Engineering School, University of Science and Technology Beijing, Beijing, China 100083 and School of Computer Science, Beijing Technology and Business University, Beijing, China 10003 ...;Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, School of Computer Science and Technology, Beijing University of Posts and Telecommunications, Beijing, China 100876;Information Engineering School, University of Science and Technology Beijing, Beijing, China 100083;Information Engineering School, University of Science and Technology Beijing, Beijing, China 100083
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Year:
2008

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A study on automatically extracted keywords in text categorization

ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics

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Abstract

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 mining 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.