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Quality Mining: A Data Mining Based Method for Data Quality Evaluation

The value of information depends directly on the quality of the data used. Decisions are no better than the data on which they are based. How can organizations assess the quality of their information? How can they know if their data are useful? Quality control and management have become competitive needs for most businesses today, and there is a wide experience on the topic of quality. Approaches range from technical, such as statistical process control, to managerial, such as quality circles. An analogous experience basis is needed for data quality. In this paper we present a method for data quality evaluation based on Data Mining. We introduce QuAsAR, a mechanism for the systematic analysis of correctness based on the information itself. In order to evaluate the performance of the method, we apply it to a real case study. This case study helps us to analyze support and confidence intervals and distribution of erroneous data.