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Multi-Relational Data Mining: An Introduction

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

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Multi-Relational Data Mining: An Introduction

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  1. Multi-Relational Data Mining: An Introduction Joe Paulowskey

  2. Overview • Introduction to Data Mining • Relational • Data • Patterns • Inductive Logic Programming (ILP) • Relational Association Rules • Relational Decision Trees • Relation Distance-Based Approaches

  3. Relation Data • Relational Database • Multiple Tables • Defined • Views • Tables

  4. Relational Pattern • Multiple Relations from a relational database • More Expressive • Opens up • Classification • Association • Regression

  5. Relational Pattern (Cont.) • Expressed in Subsets of First Order Logic

  6. 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

  7. 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

  8. Inductive Logic Programming (ILP) • Logic Programs used to find patterns • Clauses • Head and Body • Literals • Types • Definite • Program

  9. ILP (Cont) • Predicate • Relations in relational database • Arguments -> Attributes • Attributes are Typed • Database Clauses are typed program clauses • Deductive Database

  10. Relational Rule Induction ILP • Learn logical definitions of relations • Classification • Rules can be found by decision trees • Simple Algorithm • Dealing with noisy/incomplete data

  11. ILP Problems to Propositional Forms • Propositional • attribute-value • Use Single Table Data Mining algorithms • LINUS • Background Knowledge

  12. ILP/RDM Algorithms • Share • Learning as a Search Paradigm • Differences • Representation of Data, Patterns • Refinement operators • Testing Coverage • Upgrading from Propositional to Relational

  13. Relational Association Rules • Frequent Patterns • Determining Frequency • Itemsets • Association Rules • Obtained by frequent itemsets

  14. Relational Decision Trees • Used for Prediction • Binary Trees • First Order Decision List

  15. Relational Distance-Based Approaches • Calculated distance between two objects • Statistical Approaches

  16. Conclusion

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