Pattern Mining with Evolutionary Algorithms

Softcover Nachdruck of the original 1. Auflage 2016
ISBN: 978-3-319-81618-0
Verlag: Springer International Publishing

Buch, Englisch, 190 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 3168 g

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.

Zielgruppe

Research

Autoren/Hrsg.

Luna, José María

Ventura, Sebastián

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

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.