NDLI: Research of tax inspection cases-choice based on association rules in data mining

Content Provider	IEEE Xplore Digital Library
Author	Qing-Xiang Zhu Li-Juan Guo Jing Liu Nan Xu Wei-Xu Li
Copyright Year	2009
Description	Author affiliation: School of Electrical and Electric Engineering, Shanghai Jiao Tong University, 200240, China (Wei-Xu Li) \|\| Library Yanshan University, Qinhuangdao 066004, China (Jing Liu) \|\| School of Economics and Management, Yanshan University, Qinhuangdao 066004, China (Qing-Xiang Zhu; Li-Juan Guo; Nan Xu)
Abstract	Association rule of data mining mainly refers to digging out an interesting correlation, opening out the unknown inter-dependence in a great deal of the data. This paper firstly set up inspection indexes for enterprise income tax, and then applies the apriori algorithm of association rules of data mining into tax inspection cases-choice. This method can accurately find the dishonest enterprise in order to improve the efficiency and effectiveness of inspection.
Starting Page	2625
Ending Page	2628
File Size	168931
Page Count	4
File Format	PDF
ISBN	9781424437023
DOI	10.1109/ICMLC.2009.5212107
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Inspection Association rules Data mining Databases Decision making Machine learning Cybernetics Finance Data analysis Conference management Apriori algorithm Tax inspection cases-choice Indicator system
Content Type	Text
Resource Type	Article

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