Multi-Relational Data Mining: An Introduction

Joe Paulowskey

Overview

Introduction to Data Mining Relational

Data Patterns

Inductive Logic Programming (ILP) Relational Association Rules Relational Decision Trees Relation Distance-Based Approaches

Relation Data

Relational DatabaseMultiple TablesDefined

Views Tables

Relational Pattern

Multiple Relations from a relational databaseMore Expressive

Opens upClassificationAssociationRegression

Relational Pattern (Cont.)

Expressed in Subsets of First Order Logic

Data Mining

Look for patterns in data What do you discover?

Associations Sequences Classifications

Goals of Data Mining Predict Identify Classify Optimize

Uses Business Data Environmental/Traffic

Engineering Web Mining Drug Design

Data Mining: Relational Databases Most Data Mining approaches deal with

single tablesNot safe to merge multiple tables into one

single table Number of patterns increases

Explicit constraints required

Inductive Logic Programming (ILP)

Logic Programs used to find patterns Clauses

Head and BodyLiteralsTypes

Definite Program

ILP (Cont)

PredicateRelations in relational databaseArguments -> Attributes

Attributes are Typed

Database Clauses are typed program clauses

Deductive Database

Relational Rule Induction ILP

Learn logical definitions of relations Classification

Rules can be found by decision treesSimple Algorithm

Dealing with noisy/incomplete data

ILP Problems to Propositional Forms Propositional

attribute-value Use Single Table Data Mining algorithms LINUS

Background Knowledge

ILP/RDM Algorithms

ShareLearning as a Search Paradigm

DifferencesRepresentation of Data, PatternsRefinement operatorsTesting Coverage

Upgrading from Propositional to Relational

Relational Association Rules

Frequent PatternsDetermining Frequency Itemsets

Association RulesObtained by frequent itemsets

Relational Decision Trees

Used for Prediction Binary Trees First Order Decision List

Relational Distance-Based Approaches Calculated distance between two objects Statistical Approaches

Conclusion