Multi-Relational Data Mining: An Introduction
Saˇso Dˇzeroski
Joˇzef 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, multi-relational data mining (MRDM)
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 multi-relational
case and MRDM now encompasses multi-relational (MR) as-
sociation rule discovery, MR decision trees and MR distance-
based methods, among others. MRDM approaches have
been successfully applied to a number of problems in a va-
riety of areas, most notably in the area of bioinformatics.
This article provides a brief introduction to MRDM, while
the remainder of this special issue treats in detail advanced
research topics at the frontiers of MRDM.
Keywords
relational data mining, multi-relational data mining,
inductive logic programming, relational association rules,
relational decision trees, relational distance-based methods
1. IN A NUTSHELL
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 [16], many of which are based on induc-
tive logic programming (ILP, [35]), look for patterns that in-
volve multiple tables (relations) from a relational database.
To emphasize this fact, RDM is often referred to as multi-
relational data mining (MRDM, [21]). In this article, 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 ar-
eas.
1.1 Relational data
A relational database typically consists of several tables
(relations) and not just one table. The example database in
Table 1 has two relations: Customer and MarriedTo. Note
that relations can be defined extensionally (by tables, as in
our example) or intensionally through database views (as
explicit logical rules). The latter typically represent re-
lationships that can be inferred from other relationships.
For example, having extensional representations of the re-
lations mother and father, we can intensionally define the
relations grandparent, grandmother, sibling, and ancestor,
among others.
Intensional definitions of relations typically represent gen-
eral knowledge about the domain of discourse. For example,
if we have extensional relations listing the atoms that make a
compound molecule and the bonds between them, functional
groups of atoms can be defined intensionally. Such general
knowledge is called domain knowledge or background knowl-
edge.
Table 1: A relational database with two tables and two
classification rules: a propositional and a relational.
Customer table
ID Gender Age Income TotalSpent BigS
c1 Male 30 214000 18800 Yes
c2 Female 19 139000 15100 Yes
c3 Male 55 50000 12400 No
c4 Female 48 26000 8600 No
c5 Male 63 191000 28100 Yes
c6 Male 63 114000 20400 Yes
c7 Male 58 38000 11800 No
c8 Male 22 39000 5700 No
... ... ... ... ... ...
MarriedTo table
Spouse1 Spouse2
c1 c2
c2 c1
c3 c4
c4 c3
c5 c12
c6 c14
... ...
Propositional rule
IF Income > 108000 THEN BigSpender = Yes
Relational rule
big spender(C1,Age1,Income1,TotalSpent1) ←
married to(C1,C2) ∧
customer(C2,Age2,Income2,TotalSpent2,BS2) ∧
Income2 ≥ 108000.
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