Data Mining and Knowledge Discovery with Evolutionary Algorithms

✍ Scribed by Dr. Alex A. Freitas (auth.)

Publisher: Springer-Verlag Berlin Heidelberg
Year: 2002
Tongue: English
Leaves: 272
Series: Natural Computing Series
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ 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 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.

✦ Table of Contents

Front Matter....Pages i-xiv
Introduction....Pages 1-11
Data Mining Tasks and Concepts....Pages 13-43
Data Mining Paradigms....Pages 45-63
Data Preparation....Pages 65-78
Basic Concepts of Evolutionary Algorithms....Pages 79-106
Genetic Algorithms for Rule Discovery....Pages 107-137
Genetic Programming for Rule Discovery....Pages 139-163
Evolutionary Algorithms for Clustering....Pages 165-178
Evolutionary Algorithms for Data Preparation....Pages 179-204
Evolutionary Algorithms for Discovering Fuzzy Rules....Pages 205-231
Scaling up Evolutionary Algorithms for Large Data Sets....Pages 233-254
Conclusions and Research Directions....Pages 255-261
Back Matter....Pages 263-265

✦ Subjects

Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Information Storage and Retrieval; Algorithm Analysis and Problem Complexity; Database Management

📜 SIMILAR VOLUMES

A Survey of Evolutionary Algorithms for

📁 A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery

✍ Freitas A.A. 📂 Library 🌐 English

This chapter discusses the use of evolutionary algorithms, particularly genetic algorithms and genetic programming, in data mining and knowledge discovery. We focus on the data mining task of classification. In addition, we discuss some preprocessing and postprocessing steps of the knowledge discove

Knowledge Discovery and Data Mining

📁 Knowledge Discovery and Data Mining

✍ Xiao Li Wang, Wei Wu, Lei Yu (auth.), Honghua Tan (eds.) 📂 Library 📅 2012 🏛 Springer-Verlag Berlin Heidelberg 🌐 English

The volume includes a set of selected papers extended and revised from the 4th International conference on Knowledge Discovery and Data Mining, March 1-2, 2011, Macau, Chin.This Volume is to provide a forum for researchers, educators, engineers, and government officials involved

Knowledge discovery and data mining

📁 Knowledge discovery and data mining

✍ Bramer, Max A 📂 Library 📅 1999 🏛 The Institution of Electrical Engineers 🌐 English

This book reviews some of the underlying technologies and also some recent applications in a number of fields. In a world increasingly overloaded with data of varying quality, not least via the Internet, computerised tools are becoming useful to ''mine'' useful data from the mass available

Data Clustering: Algorithms and Applicat

📁 Data Clustering: Algorithms and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

✍ Charu C. Aggarwal (editor), Chandan K. Reddy (editor) 📂 Library 📅 2013 🏛 Chapman and Hall/CRC 🌐 English

Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire ar

Data Mining and Knowledge Discovery via

📁 Data Mining and Knowledge Discovery via Logic-Based Methods: Theory, Algorithms, and Applications

✍ Evangelos Triantaphyllou (auth.) 📂 Library 📅 2010 🏛 Springer US 🌐 English

The importance of having efficient and effective methods for data mining and knowledge discovery (DM) is rapidly growing. This is due to the wide use of fast and affordable computing power and data storage media and also the gathering of huge amounts of data in almost all aspects of human acti

Data Mining and Knowledge Discovery via

📁 Data Mining and Knowledge Discovery via Logic-Based Methods: Theory, Algorithms, and Applications

✍ Evangelos Triantaphyllou 📂 Library 📅 2010 🏛 Springer US 🌐 English

The importance of having ef cient and effective methods for data mining and kn- ledge discovery (DM&KD), to which the present book is devoted, grows every day and numerous such methods have been developed in recent decades. There exists a great variety of different settings for the main problem stud