Overview
- Covers the use of Evolutionary Computation techniques to pattern mining problems
- Uses algorithms that have been integrated into the well-known WEKA software for free use
- Offers formal definitions about patterns, patterns mining, type of patterns and the usefulness of patterns in the knowledge discovery process
- Includes supplementary material: sn.pub/extras
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Table of contents (9 chapters)
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Front Matter
Keywords
- Pattern Mining
- Association Rule Mining
- Frequent Pattern Mining
- Infrequent Pattern Mining
- Evolutionary Algorithms
- Exceptional relationships
- Pattern mining quality measures
- Subgroup discovery
- Genetic Algorithms
- Genetic Programming
- Multi-objective Evolutionary Algorithms
- Hybrid Algorithms
- Continuous Patterns
- Negative Association Rules
- Supervised Local Search
- algorithm analysis and problem complexity
About this book
This book offers a survey on evolutionary computation with particular emphasis on genetic algorithms and genetic programming. Also included is an analysis of the set of quality measures most widely used in the field of pattern mining with evolutionary algorithms. This book serves as a review of the most important evolutionary algorithms for pattern mining. It considers the analysis of different algorithms for mining different type of patterns and relationships between patterns, such as frequent patterns, infrequent patterns, patterns defined in a continuous domain, or even positive and negative patterns.
A completely new problem in the pattern mining field, mining of exceptional relationships between patterns, is discussed. In this problem the goal is to identify patterns which distribution is exceptionally different from the distribution in the complete set of data records. Finally, the book deals with the subgroup discovery task, a method to identify a subgroup of interesting patterns that is related to a dependent variable or target attribute. This subgroup of patternssatisfies two essential conditions: interpretability and interestingness.
Reviews
“Pattern Mining with Evolutionary Algorithms provides an overview of methods using evolutionary algorithms for discovering interesting patterns. The book is very useful and can potentially attract more people to carry out research and applications in pattern mining using evolutionary algorithms. … I found it easy to read, well-written and well-structured, very beneficial and important for readers to develop substantial learning. In view of this, I strongly recommend this valuable book.” (Bing Xue, Genetic Programming and Evolvable Machines, Vol. 18, 2017)
Authors and Affiliations
About the authors
Bibliographic Information
Book Title: Pattern Mining with Evolutionary Algorithms
Authors: Sebastián Ventura, José María Luna
DOI: https://doi.org/10.1007/978-3-319-33858-3
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-33857-6Published: 21 June 2016
Softcover ISBN: 978-3-319-81618-0Published: 07 June 2018
eBook ISBN: 978-3-319-33858-3Published: 13 June 2016
Edition Number: 1
Number of Pages: XIII, 190
Number of Illustrations: 122 b/w illustrations, 4 illustrations in colour
Topics: Pattern Recognition, Data Mining and Knowledge Discovery, Algorithm Analysis and Problem Complexity