Discovering informative patterns and data cleaning
Advances in knowledge discovery and data mining
Data quality and data cleaning: an overview
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
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In a real world data set there are usually four kinds of mistaken values, the first one is the mistake in unit; the second one is the mistake of putting the radix points in wrong place, the third one is a scribal error, and the fourth one is a computational mistake. In this paper, we propose two algorithms for finding these four kinds of mistaken data. SARS and coronary heart disease data sets experimental results show that the two algorithms are available, that is, using the two algorithms we find some mistakes in the SARS and coronary heart disease data sets, and the results correspond to that found manually by medical experts.