Abstract
Data Mining algorithms look for patterns in data. While most existing Data Mining approaches look for patterns in a single data table, relational Data Mining (RDM) approaches look for patterns that involve multiple tables (relations) from a relational database. In recent years, the most common types of patterns and approaches considered in Data Mining have been extended to the relational case and RDM now encompasses relational association rule discovery and relational decision tree induction, among others. RDM approaches have been successfully applied to a number of problems in a variety of areas, most notably in the area of bioinformatics. This chapter provides a brief introduction to RDM.
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Džeroski, S. (2005). Relational Data Mining. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/0-387-25465-X_41
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DOI: https://doi.org/10.1007/0-387-25465-X_41
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