Abstract
Relational data mining algorithms and systems are capable of directly dealing with multiple tables or relations as they are found in today’s relational databases. This reduces the need for manual preprocessing and allows problems to be treated that cannot be handled easily with standard single-table methods. This paper provides a tutorial-style introduction to the topic, beginning with a detailed explanation of why and where one might be interested in relational analysis. We then present the basics of Inductive Logic Programming (ILP), the scientific field where relational methods are primarily studied. After illustrating the workings of MiDOS, a relational methods for subgroup discovery, in more detail, we show how to use relational methods in one of the current data mining systems.
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Wrobel, S. (2001). Inductive Logic Programming for Knowledge Discovery in Databases. In: Džeroski, S., Lavrač, N. (eds) Relational Data Mining. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04599-2_4
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DOI: https://doi.org/10.1007/978-3-662-04599-2_4
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