Data Mining and Knowledge Discovery with Evolutionary Algorithms

Description

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.


Contents:

Introduction
Data mining tasks and concepts
Data mining paradigms
Data preparation
Basic concepts of evolutionary algorithms
Genetic algorithms for rule dicovery
Genetic programming for rule discovery
Evolutionary algorithms for clustering
Evolutionary algorithms for data preparation
Evolutionary algorithms for discovery fuzzy rules
Scaling up evolutionary algorithms fdor large data sets
Conclusions and research directions