Data Mining and Knowledge Discovery with Evolutionary Algorithms

Name: Data Mining and Knowledge Discovery with Evolutionary Algorithms
Brand: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
SKU: 9783642077630
Price: 100.0 EUR
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Autor: Alex A Freitas

Jazyk:

Väzba: Brožovaná

Vydavateľ: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

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This book integrates two areas of computer science, namely data mining and evolutionary algorithms....

Informácie o knihe

Autor

Alex A Freitas

Jazyk

Angličtina

Väzba

Kniha - Brožovaná

Vydalo

2011

Stránok

265

EAN

9783642077630

ISBN

3642077633

Enbook ID

01229636

Vydavateľ

Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Hmotnosť

433

Rozmery

155 x 235 x 22

Kompletný popis

This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an active research area. In general, data mining consists of extracting knowledge from data. The motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions. This book emphasizes the importance of discovering comprehensible, interesting knowledge, which is potentially useful for intelligent decision making. The text explains both basic concepts and advanced topicsThis book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an area of active research. In general, data mining consists of extracting knowledge from data. In this book we particularly emphasize the importance of discovering comprehensible and interesting knowledge, which is potentially useful to the reader for intelligent decision making. In a nutshell, the motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions (rules or another form of knowledge representation). In contrast, most rule induction methods perform a local, greedy search in the space of candidate rules. Intuitively, the global search of evolutionary algorithms can discover interesting rules and patterns that would be missed by the greedy search. This book presents a comprehensive review of basic concepts on both data mining and evolutionary algorithms and discusses significant advances in the integration of these two areas. It is self-contained, explaining both basic concepts and advanced topics.

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Data Mining and Knowledge Discovery with Evolutionary Algorithms

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