Affiliations: Department of Computer Oriented Statistics and Data Analysis, University of Augsburg, Augsburg, Germany
Correspondence:
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Corresponding author: Waqas Ahmed Malik, Department of Computer Oriented Statistics and Data Analysis, Institute of Mathematics, University of Augsburg, Universittsstrasse 14, D-86159 Augsburg, Germany. Tel.: +49 821 598 2236; Fax: +49 821 598 2200; E-mail: [email protected].
Abstract: High data quality is important for every application. Inaccurate or inadequate data can lead to inappropriate assumptions, misleading results, bias and ultimately poor policy and decision making. Finding errors and cleaning data is a time consuming process. This paper presents a framework for automatically detecting unusual and erroneous data values in datasets. The main idea is to generate association rules with very high confidence and to identify the cases that are exceptions to these rules. Experimental results show that the proposed framework is able to successfully identify erroneous values in large datasets.
Keywords: Data quality, data cleaning, error detection, outlier detection, association rules, data mining, market basket
