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.

中文翻译：

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通过基于低频关联的数据挖掘对大型土地质量数据库进行错误检查

土地质量，特别是耕地质量数据库的准确性，是土地质量评估和土地退化评估的先决条件。土地质量数据库的错误检查是确保这些土地质量数据库准确性的重要一步。现有方法在土地质量数据库的错误检查中没有考虑数据元素之间的内在联系。本文探索了一种通过使用数据库中存在的内在关系进行土地质量数据库错误检查的新思路。这个想法背后的主要假设是，数据库错误倾向于在低频发生，并以低频关联的形式存在于数据库中。因此，可以通过分析数据库中数据项之间的组合关系来定位这些错误。基于这种思想，本文开发了一种通过数据挖掘的低频数据关联（LFDA）方法。对照实验的结果表明，LFDA可有效地定位引入土地质量数据库的误差。使用广州土地质量数据库进行的应用实验进一步证实了这一发现。这项研究为土地质量数据库的错误检查开辟了一种新的重要途径。