TABLE OF CONTENTS Sa o D eroski
Jo ef Stefan Institute
Jamova 39, SI-1000 Ljubljana, Slovenia
saso.dzeroski@ijs.si
| 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. |
| Keywords: | relational Data Mining, inductive logic programming, relational association rules, relational decision trees |
Data Mining algorithms look for patterns in data. Most existing Data Mining approaches are propositional and look for patterns in a single data table. Relational Data Mining (RDM) approaches (D eroski and Lavra?, 2001), many of which are based on inductive logic programming (Muggleton, 1992), look for patterns that involve multiple tables (relations) from a relational database. To emphasize this fact, RDM is often referred to as multi-relational data mining (D eroski et al., 2002). In this chapter, we will use the terms RDM and MRDM interchangeably. In this introductory section, we take a look at data, patterns, and algorithms in RDM, and mention some application areas.
A relational database typically consists...
