Overview
- State of the art in the area of Data Mining and Knowledge Discovery with Evolutionary Algorithms
- Demonstrates how the different tools of evolutionary computation can be used for solving real-life problems in data mining and bioinformatics
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 163)
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About this book
Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting to the user. The total process is highly computation intensive. The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search in the space of candidate solutions (for instance, rules or another form of knowledge representation).
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Keywords
Table of contents (12 chapters)
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Front Matter
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Back Matter
Editors and Affiliations
Bibliographic Information
Book Title: Evolutionary Computation in Data Mining
Editors: Ashish Ghosh, Lakhmi C. Jain
Series Title: Studies in Fuzziness and Soft Computing
DOI: https://doi.org/10.1007/3-540-32358-9
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2005
Hardcover ISBN: 978-3-540-22370-2Published: 18 October 2004
Softcover ISBN: 978-3-642-42195-2Published: 15 November 2014
eBook ISBN: 978-3-540-32358-7Published: 22 June 2006
Series ISSN: 1434-9922
Series E-ISSN: 1860-0808
Edition Number: 1
Number of Pages: XVIII, 266
Topics: Artificial Intelligence, Mathematical and Computational Engineering, Database Management