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Error checking of large land quality databases through data mining based on low frequency associations

文献类型：期刊论文

作者Qiu, Xiao-Qian1; Zhu, A-Xing2,3,4,5,6; Hu, Yue-Ming1,6,7,8,9; Guo, Yu-Bin9; Shen, Xiao-Wen9
刊名LAND DEGRADATION & DEVELOPMENT
出版日期2020-03-23
页码10
关键词data mining error checking land quality assessment land quality database low-frequency data associations
ISSN号1085-3278
DOI10.1002/ldr.3581
通讯作者Hu, Yue-Ming(yueminghugis@163.com)
英文摘要The accuracy of databases on land quality, particularly on cultivated land quality, is a prerequisite for land quality assessment and land degradation evaluation. Error checking of land quality databases is an important step in ensuring the accuracy of these land quality databases. The existing methods do not consider the intrinsic relationships among data elements in error checking of land quality databases. This paper explores a new idea for error checking of land quality database through the use of intrinsic relationships that existed in the database. The main assumption behind this idea is that database errors tend to occur at low frequencies and exist as low-frequency associations with other data items in a database. Thus, these errors can be located by analyzing the combinational relationships between the data items in the database. Based on this idea a new method, low-frequency data associations (LFDA) through data mining was developed in this paper. The results from control experiments shows that LFDA is effective in locating errors introduced into a land quality database. The applied experiment using the Guangzhou land quality database further confirmed this finding. This research opens a new and significant way for error checking of land quality databases.
WOS关键词IMAGES
资助项目Guangzhou Science and Technology Project, China[201804020034] ; Guangdong Provincial Science and Technology Project of China[2017A050501031] ; National Key Research and Development Program of China[2016YFC0501801] ; National Natural Science Foundation of China[41871300] ; Qinghai Science and Technology Project, China[2017-ZJ-730]
WOS研究方向Environmental Sciences & Ecology ; Agriculture
语种英语
WOS记录号WOS:000551204400001
出版者WILEY
资助机构Guangzhou Science and Technology Project, China ; Guangdong Provincial Science and Technology Project of China ; National Key Research and Development Program of China ; National Natural Science Foundation of China ; Qinghai Science and Technology Project, China
源URL[http://ir.igsnrr.ac.cn/handle/311030/158296]  
专题中国科学院地理科学与资源研究所
通讯作者Hu, Yue-Ming
作者单位1.South China Agr Univ, Coll Nat Resources & Environm, Guangzhou 510642, Peoples R China
2.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Peoples R China
3.Nanjing Normal Univ, Sch Geog, Nanjing, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
5.Univ Wisconsin, Dept Geog, Madison, WI 53706 USA
6.South China Acad Nat Resources Sci & Technol, Bur Civil Affairs Guangzhou Municipal, Guangzhou, Peoples R China
7.Guangdong Prov Engn Res Ctr Land Informat Technol, Dept Sci & Technol Guangdong Prov, Guangzhou, Peoples R China
8.Guangdong Prov Key Lab Land Use & Consolidat, Dept Sci & Technol Guangdong Prov, Guangzhou, Peoples R China
9.South China Agr Univ, Coll Math & Informat, Guangzhou, Peoples R China
推荐引用方式
GB/T 7714		 Qiu, Xiao-Qian,Zhu, A-Xing,Hu, Yue-Ming,et al. Error checking of large land quality databases through data mining based on low frequency associations[J]. LAND DEGRADATION & DEVELOPMENT,2020:10.
APA Qiu, Xiao-Qian,Zhu, A-Xing,Hu, Yue-Ming,Guo, Yu-Bin,&Shen, Xiao-Wen.(2020).Error checking of large land quality databases through data mining based on low frequency associations.LAND DEGRADATION & DEVELOPMENT,10.
MLA Qiu, Xiao-Qian,et al."Error checking of large land quality databases through data mining based on low frequency associations".LAND DEGRADATION & DEVELOPMENT (2020):10.

入库方式： OAI收割

来源：地理科学与资源研究所

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