Error checking of large land quality databases through data mining based on low frequency associations. (23rd March 2020)

• Record Type: Journal Article
• Authors: Qiu, Xiao‐Qian; Zhu, A‐Xing; Hu, Yue‐Ming; Guo, Yu‐Bin; Shen, Xiao‐Wen
• Is Part Of: Land degradation & development. Volume 31:Number 15(2020)
• Page Start: 2146
• Page End: 2155
• Publication Date: 2020-03-23
• Subjects: data mining -- error checking -- land quality assessment -- land quality database -- low‐frequency data associations; Land degradation -- Periodicals; Soil conservation -- Periodicals; Reclamation of land -- Periodicals; Land use -- Periodicals; Economic development -- Environmental aspects -- Periodicals; 333.7315
• DOI: 10.1002/ldr.3581
• Languages: English
• ISSNs: 1085-3278

Abstract

Abstract: 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.