Pattern Mining with Evolutionary Algorithms - Sebastián Ventura, José María Luna

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Pattern Mining with Evolutionary Algorithms (eBook)

Sebastián Ventura, José María Luna (Autoren)

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2016 | 1st ed. 2016
XIII, 190 Seiten
Springer International Publishing (Verlag)
978-3-319-33858-3 (ISBN)

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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 patterns satisfies two essential conditions: interpretability and interestingness.

Erscheint lt. Verlag	13.6.2016
Zusatzinfo	XIII, 190 p. 126 illus., 4 illus. in color.
Verlagsort	Cham
Sprache	englisch
Themenwelt	Mathematik / Informatik ► Informatik ► Datenbanken
Themenwelt	Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge
Schlagworte	Algorithm analysis and problem complexity • Association Rule Mining • Continuous Patterns • evolutionary algorithms • Exceptional relationships • frequent pattern mining • Genetic algorithms • genetic programming • hybrid algorithms • Infrequent Pattern Mining • Multi-Objective Evolutionary Algorithms • Negative Association Rules • pattern mining • Pattern mining quality measures • Subgroup discovery • Supervised Local Search
ISBN-10	3-319-33858-7 / 3319338587
ISBN-13	978-3-319-33858-3 / 9783319338583

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