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Other titles in the Natural Computing series:

Data Mining and Knowledge Discovery with Evolutionary Algorithms (Natural Computing Series)

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Data Mining and Knowledge Discovery with Evolutionary Algorithms (Natural Computing Series) Cover

 

Synopses & Reviews

Publisher Comments:

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.

Synopsis:

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, interesting knowledge, which is potentially useful for 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. 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.

Table of Contents

Preface; 1. Introduction; 2. Data Mining Tasks and Concepts; 3. Data Mining Paradigms; 4. Data Prepration; 5. Basic Concepts of Evolutionary Algorithms; 6. Genetic Algorithms for Rule Discovery; 7. Genetic Programming for Rule Discovery and Decision-Tree Building; 8. Evolutionary Algorithms for Clustering; 9. Evolutionary Algorithms for Data Preparation; 10. Evolutionary Algorithms for Discovering Fuzzy Rules; 11. Scaling up Evolutionary Algorithms for Large Data Sets; 12. Conclusions and Research Directions; Index.

Product Details

ISBN:
9783540433316
Author:
Freitas, Alex A.
Publisher:
Springer
Location:
Berlin, Heidelberg
Subject:
Data mining
Subject:
Database searching
Subject:
Computer algorithms
Subject:
Programming - Algorithms
Subject:
Database Management - Database Mining
Subject:
Artificial Intelligence - General
Subject:
Intelligence (AI) & Semantics
Subject:
Computers-Reference - General
Subject:
Artificial Intelligence
Subject:
Computing Methodologies.
Subject:
Evolutionary Algorithms
Subject:
Machine learning
Subject:
Artificial Intelligence (incl. Robotics)
Subject:
Data Mining and Knowledge Discovery
Subject:
Information storage and retrieval.
Subject:
Algorithm Analysis and Problem Complexity
Subject:
Database management
Subject:
Computer Science
Subject:
B
Subject:
Information storage and retrieva
Subject:
Computer software
Copyright:
Edition Description:
Book
Series:
Natural Computing Series
Series Volume:
2000
Publication Date:
20021003
Binding:
HARDCOVER
Language:
English
Illustrations:
Yes
Pages:
279
Dimensions:
235 x 155 mm 1260 gr

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » General
Computers and Internet » Database » Design
Computers and Internet » Database » General
Computers and Internet » Internet » Information
Computers and Internet » Software Engineering » Algorithms
Computers and Internet » Software Engineering » Programming and Languages
Computers and Internet » Software Engineering » Software Management
Health and Self-Help » Health and Medicine » Medical Specialties
Science and Mathematics » Electricity » General Electronics
Science and Mathematics » Mathematics » Applied
Science and Mathematics » Mathematics » Topology

Data Mining and Knowledge Discovery with Evolutionary Algorithms (Natural Computing Series) New Hardcover
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Product details 279 pages Springer-Verlag - English 9783540433316 Reviews:
"Synopsis" by , 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, interesting knowledge, which is potentially useful for 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. 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.
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