Pattern Mining with Evolutionary Algorithms

Author:   Sebastián Ventura ,  José María Luna
Publisher:   Springer International Publishing AG
Edition:   Softcover reprint of the original 1st ed. 2016
ISBN:  

9783319816180


Pages:   190
Publication Date:   07 June 2018
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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Pattern Mining with Evolutionary Algorithms


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Overview

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 discovery process suffers from both high runtime and memory requirements, especially when high dimensional datasets are analyzed. To solve this issue, many pruning strategies have been developed. Nevertheless, with the growing interest in the storage of information, more and more datasets comprise such a dimensionality that the discovery of interesting patterns becomes a challenging process. In this regard, the use of evolutionary algorithms for mining pattern enables the computation capacity to be reduced, providing sufficiently good solutions. 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.    

Full Product Details

Author:   Sebastián Ventura ,  José María Luna
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   Softcover reprint of the original 1st ed. 2016
Dimensions:   Width: 15.50cm , Height: 1.10cm , Length: 23.50cm
Weight:   3.168kg
ISBN:  

9783319816180


ISBN 10:   3319816187
Pages:   190
Publication Date:   07 June 2018
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Introduction to Pattern Mining.- Quality Measures in Pattern Mining.- Introduction to Evolutionary Computation.- Pattern Mining with Genetic Algorithms.- Genetic Programming in Pattern Mining.- Multiobjective Approaches in Pattern Mining.- Supervised Local Pattern Mining.- Mining Exceptional Relationships Between Patterns.- Scalability in Pattern Mining.   

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)


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