Freitas A.A. Data Mining and Knowledge Discovery with Evolutionary Algorithms

Springer, 2002. — 272 p.This book addresses the integration of 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 essence, data mining consists of extracting valid, comprehensible, and interesting knowledge from data. Data mining is actually an interdisciplinary field, since there are many kinds of methods that can be used to extract knowledge from data. Arguably, data mining mainly uses methods from machine learning (a branch of artificial intelligence) and statistics (including statistical pattern recognition). Our discussion of data mining and evolutionary algorithms is primarily based on machine learning concepts and principles. In particular, in this book we emphasize the importance of discovering comprehensible, interesting knowledge, which the user can potentially use to make intelligent decisions.
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 initially presents a comprehensive review of basic concepts from both data mining and evolutionary algorithms, and then it discusses significant advances in the integration of these two areas. It is a self-contained book, explaining both basic concepts and advanced topics in a clear and informal style.Data Mining Tasks and Concepts
Data Mining Paradigms
Data Preparation
Basic Concepts of Evolutionary Algorithms
Genetic Algorithms for Rule Discovery
Genetic Programming for Rule Discovery
Evolutionary Algorithms for Clustering
Evolutionary Algorithms for Data Preparation
Evolutionary Algorithms for Discovering Fuzzy Rules
Scaling up Evolutionary Algorithms for Large Data Sets
Conclusions and Research Directions