Data Mining and Knowledge Discovery with Evolutionary Algorithms

Alex A. Freitas

ISBN 10: 3540433317 / ISBN 13: 9783540433316

Editore: Springer Berlin Heidelberg, 2002

Riguardo questo articolo

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book integrates two areas of computer science, namely data mining and evolutionary algorithms (EAs). In essence, data mining consists of extracting interesting knowledge from data. The book presents a comprehensive review of both data mining and EAs an.

Codice articolo 4890387

Dati bibliografici

• Titolo: Data Mining and Knowledge Discovery with Evolutionary Algorithms
• Editore: Springer Berlin Heidelberg
• Data di pubblicazione: 2002
• Lingua: Inglese
• ISBN 10: 3540433317
• ISBN 13: 9783540433316
• Rilegatura: Gebunden
• Condizione: New
• Cataloghi dei venditori:
  • Mathematik/Naturwissenschaften/Technik/Medizin

Descrizione articolo

Riassunto

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 topics

Dalla quarta di copertina

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 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.