Introduction The dramatic increase in available computer storage capacity over the last 10 years has led to the creation of very large databases of scientific and commercial information. The need to analyze these masses of data has led to the evolution of the new field knowledge discovery in databases (KDD) at the intersection of machine learning, statistics and database technology. Being interdisciplinary by nature, the field offers the opportunity to combine the expertise of different fields into a common objective.Moreover,within each field diverse methods have been developed and justified with respect to different quality criteria. We have toinvestigatehowthesemethods can contribute to solving the problem of KDD. Traditionally, KDD was seeking to find global models for the data that - plain most of the instances of the database and describe the general structure of the data. Examples are statistical time series models, cluster models, logic programs with high coverage or classification models like decision trees or linear decision functions. In practice, though, the use of these models often is very limited, because global models tend to find only the obvious patterns in the data, 1 which domain experts already are aware of. What is really of interest to the users are the local patterns that deviate from the already-known background knowledge. David Hand, who organized a workshop in 2002, proposed the new field of local patterns.