Modern information systems consist of many distributed computer and database systems. The integration of such distributed data into a single data warehouse system is confronted with the well known problem of low data quality. In this paper we present an approach that facilitates a dynamic identification of spurious and error-prone data stored in a large data warehouse. The identification of data quality problems is based on data mining techniques, such as clustering, subspace clustering and classification. Furthermore, we present via a case study the applicability of our approach on real data. The experimental results show that our approach efficiently identifies data quality problems.