Multi-Relational Data Mining: An Introduction Joe Paulowskey.

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Presentation transcript:

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 Database  Multiple Tables  Defined Views Tables

Relational Pattern Multiple Relations from a relational database  More Expressive Opens up  Classification  Association  Regression

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 tables  Not 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 Body  Literals  Types Definite Program

ILP (Cont) Predicate  Relations in relational database  Arguments -> 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 trees  Simple 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 Share  Learning as a Search Paradigm Differences  Representation of Data, Patterns  Refinement operators  Testing Coverage Upgrading from Propositional to Relational

Relational Association Rules Frequent Patterns  Determining Frequency  Itemsets Association Rules  Obtained 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