Pattern Mining with Evolutionary Algorithms

• Main Authors: Ventura, Sebastián, Luna, José María
• Format: eBook
• Language: English
• Published: Cham Springer International Publishing AG 2016; Springer International Publishing; Springer
• Edition: 1
• Subjects: Algorithm Analysis and Problem Complexity; Computer Science; Computer software; Data Mining and Knowledge Discovery; Pattern Recognition
• ISBN: 9783319338576; 3319338579
• DOI: 10.1007/978-3-319-33858-3

This book provides a comprehensive overview of the field of pattern mining with evolutionary algorithms. To do so, it covers formal definitions about patterns, patterns mining, type of patterns and the usefulness of patterns in the knowledge discovery process. As it is described within the book, the...

Table of Contents:

• 6.2.2 Quality Indicators of the Pareto Front -- 6.2.3 Quality Measures to Optimize in Pattern Mining -- 6.3 Algorithmic Approaches -- 6.3.1 Genetic Algorithms -- 6.3.2 Genetic Programming -- 6.3.3 Other Algorithms -- 6.4 Successful Applications -- References -- 7 Supervised Local Pattern Mining -- 7.1 Introduction -- 7.2 Subgroup Discovery -- 7.2.1 Problem Definition -- 7.2.2 Quality Measures -- 7.2.3 Deterministic Algorithms -- 7.2.4 Evolutionary Algorithms -- 7.3 Other Supervised Local Pattern Mining Approaches -- References -- 8 Mining Exceptional Relationships Between Patterns -- 8.1 Introduction -- 8.2 Mining the Exceptionableness -- 8.2.1 Exceptional Model Mining Problem -- 8.2.2 Exceptional Relationship Mining -- 8.3 Algorithmic Approach -- 8.4 Successful Applications -- References -- 9 Scalability in Pattern Mining -- 9.1 Introduction -- 9.2 Traditional Methods for Speeding Up the Mining Process -- 9.2.1 The Role of Evolutionary Computation in Scalability Issues -- 9.2.2 Parallel Algorithms -- 9.2.3 New Data Structures -- 9.3 New Trends in Pattern Mining: Scalability Issues -- References
• Intro -- Preface -- Acknowledgments -- Contents -- 1 Introduction to Pattern Mining -- 1.1 Definitions -- 1.2 Type of Patterns -- 1.2.1 Frequent and Infrequent Patterns -- 1.2.2 Closed and Maximal Frequent Patterns -- 1.2.3 Positive and Negative Patterns -- 1.2.4 Continuous Patterns -- 1.2.5 Colossal Patterns -- 1.2.6 Sequential Patterns -- 1.2.7 Spatio-Temporal Patterns -- 1.3 Pattern Space Pruning -- 1.4 Traditional Approaches for Pattern Mining -- 1.5 Association Rules -- References -- 2 Quality Measures in Pattern Mining -- 2.1 Introduction -- 2.2 Objective Interestingness Measures -- 2.2.1 Quality Properties of a Measure -- 2.2.2 Relationship Between Quality Measures -- 2.2.3 Other Quality Properties -- 2.3 Subjective Interestingness Measures -- References -- 3 Introduction to Evolutionary Computation -- 3.1 Introduction -- 3.2 Genetic Algorithms -- 3.2.1 Standard Procedure -- 3.2.2 Individual Representation -- 3.2.3 Genetic Operators -- 3.3 Genetic Programming -- 3.3.1 Individual Representation -- 3.3.2 Genetic Operators -- 3.3.3 Code Bloat -- 3.4 Other Bio-Inspired Algorithms -- References -- 4 Pattern Mining with Genetic Algorithms -- 4.1 Introduction -- 4.2 General Issues -- 4.2.1 Pattern Encoding -- 4.2.2 Genetic Operators -- 4.2.3 Fitness Function -- 4.3 Algorithmic Approaches -- 4.4 Successful Applications -- References -- 5 Genetic Programming in Pattern Mining -- 5.1 Introduction -- 5.2 General Issues -- 5.2.1 Canonical Genetic Programming -- 5.2.2 Syntax-Restricted Programming -- 5.3 Algorithmic Approaches -- 5.3.1 Frequent Patterns -- 5.3.2 Infrequent Patterns -- 5.3.3 Highly Optimized Continuous Patterns -- 5.3.4 Mining Patterns from Relational Databases -- 5.4 Successful Applications -- References -- 6 Multiobjective Approaches in Pattern Mining -- 6.1 Introduction -- 6.2 General Issues -- 6.2.1 Multiobjective Optimization