Data Quality Mining - Making a Virute of Necessity

In this paper we introduce data quality mining (DQM) as a new and promising data mining approach from the academic and the business point of view. The goal of DQM is to employ data mining methods in order to detect, quantify, explain and correct data quality deficiencies in very large databases. Data quality is crucial for many applications of knowledge discovery in databases (KDD). So a typical application scenario for DQM is to support KDD projects, especially during the initial phases. Moreover, improving data quality is also a burning issue in many areas outside KDD. That is, DQM opens new and promising application fields for data mining methods outside the field of pure data analysis. To give a first impression of a concrete DQM approach, we describe how to employ association rules for the purpose of DQM.