Multi-Relational Data Mining

✍ Scribed by A.J. Knobbe

• Publisher: IOS Press
• Year: 2006
• Tongue: English
• Leaves: 129
• Series: Frontiers in Artificial Intelligence and Applications 145
• Category: Library

✦ Synopsis

With the increased possibilities in modern society for companies and institutions to gather data cheaply and efficiently, the subject of Data Mining has become of increasing importance. This interest has inspired a rapidly maturing research field with developments both on a theoretical, as well as on a practical level with the availability of a range of commercial tools. Unfortunately, the widespread application of this technology has been limited by an important assumption in mainstream Data Mining approaches. This assumption - all data resides, or can be made to reside, in a single table - prevents the use of these Data Mining tools in certain important domains, or requires considerable massaging and altering of the data as a pre-processing step. This limitation has spawned a relatively recent interest in richer Data Mining paradigms that do allow structured data as opposed to the traditional flat representation. This publication goes into the different uses of Data Mining, with Multi-Relational Data Mining (MRDM), the approach to Structured Data Mining, as the main subject of this book.

✦ Table of Contents

Title page......Page 2
Contents......Page 6
Acknowledgements......Page 10
Introduction......Page 12
Data Mining......Page 13
Propositional Data Mining......Page 14
Structured Data Mining......Page 15
Outline of this text......Page 17
Structured Data......Page 20
Search......Page 21
Structured Data Mining Paradigms......Page 23
A Comparison......Page 24
What's in a Name?......Page 27
Structured Data in Relational Form......Page 28
Multi-Relational Data Models......Page 29
Tables and their Roles......Page 31
Directions......Page 33
Local Structure......Page 36
Pattern language......Page 37
Refinements......Page 38
Characteristics of Multi-Relational Patterns......Page 40
Numeric Data......Page 42
Discretisation......Page 43
Rule Discovery......Page 46
Implementation......Page 48
Experiments......Page 50
Related Work......Page 53
Multi-Relational Decision Tree Induction......Page 56
Extended Selection Graphs......Page 57
Refinements......Page 59
Multi-Relational Decision Trees......Page 61
Look-Ahead......Page 62
MRDTL......Page 63
Mr-SMOTI......Page 64
Aggregate Functions......Page 66
Aggregation......Page 67
Aggregate Functions & Association-width......Page 69
Aggregate Functions & Propositionalisation......Page 72
Propositionalisation......Page 73
The RollUp Algorithm......Page 74
Musk......Page 75
Mutagenesis......Page 76
Propositionalisation......Page 77
Related Work......Page 78
Aggregate Functions & Rule Discovery......Page 80
Generalised Selection Graphs......Page 81
Refinement Operator......Page 82
Mutagenesis......Page 84
Financial......Page 85
An MRDM Architecture......Page 86
Data Mining Primitives......Page 87
Association Refinement......Page 88
Nominal Condition Refinement......Page 89
Numeric Condition Refinement......Page 90
Extended Selection Graphs......Page 91
Nominal Condition Refinement......Page 92
Numeric Condition Refinement......Page 94
AggregateCrossTable......Page 95
An MRDM Project Blueprint......Page 96
Data Understanding......Page 99
Data Preparation......Page 100
An MRDM Pre-processing Consultant......Page 101
Denormalise......Page 102
Reverse Pivot......Page 103
Create Indexes......Page 105
Contributions......Page 108
Validity of MRDM Approach......Page 109
Overview of Algorithms......Page 110
Conclusion......Page 111
Pattern Languages......Page 112
Improved Search......Page 114
Appendix A: MRML......Page 118
Bibliography......Page 120
Index......Page 126