Studies in Computational Intelligence

Foundations of Computational Intelligence

Volume 4: Bio-Inspired Data Mining

Editors: Abraham, Ajith, Hassanien, Aboul-Ella, Carvalho, André Ponce de Leon F. de (Eds.)

  • Fourth volume of a reference work on the foundations of Computational Intelligence
  • This volume is devoted to bio-inspired data mining
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About this book

Recent advances in the computing and electronics technology, particularly in sensor devices, databases and distributed systems, are leading to an exponential growth in the amount of data stored in databases. It has been estimated that this amount doubles every 20 years. For some applications, this increase is even steeper. Databases storing DNA sequence, for example, are doubling their size every 10 months. This growth is occurring in several applications areas besides bioinformatics, like financial transactions, government data, environmental monitoring, satellite and medical images, security data and web. As large organizations recognize the high value of data stored in their databases and the importance of their data collection to support decision-making, there is a clear demand for sophisticated Data Mining tools. Data mining tools play a key role in the extraction of useful knowledge from databases. They can be used either to confirm a particular hypothesis or to automatically find patterns. In the second case, which is related to this book, the goal may be either to describe the main patterns present in dataset, what is known as descriptive Data Mining or to find patterns able to predict behaviour of specific attributes or features, known as predictive Data Mining. While the first goal is associated with tasks like clustering, summarization and association, the second is found in classification and regression problems.

Computational tools or solutions based on intelligent systems are being used with great success in Data Mining applications. Nature has been very successful in providing clever and efficient solutions to different sorts of challenges and problems posed to organisms by ever-changing and unpredictable environments. It is easy to observe that strong scientific advances have been made when issues from different research areas are integrated. A particularly fertile integration combines biology and computing. Computational tools inspired on biological process can be found in a large number of applications. One of these applications is Data Mining, where computing techniques inspired on nervous systems; swarms, genetics, natural selection, immune systems and molecular biology have provided new efficient alternatives to obtain new, valid, meaningful and useful patterns in large datasets.

This Volume comprises of 16 chapters, including an overview chapter, providing an up-to-date and state-of-the research on the application of Bio-inspired techniques for Data Mining.

Table of contents (16 chapters)

  • Adaptive and Self-adaptive Techniques for Evolutionary Forecasting Applications Set in Dynamic and Uncertain Environments

    Wagner, Neal (et al.)

    Pages 3-21

  • Sequence Pattern Mining

    Zhou, Huiyu (et al.)

    Pages 23-48

  • Growing Self-Organizing Map for Online Continuous Clustering

    Smith, Toby (et al.)

    Pages 49-83

  • Synthesis of Spatio-temporal Models by the Evolution of Non-uniform Cellular Automata

    Romano, Ana L. T. (et al.)

    Pages 85-104

  • Genetic Selection Algorithm and Cloning for Data Mining with GMDH Method

    Jirina, Marcel (et al.)

    Pages 107-125

Buy this book

eBook $189.00
price for USA (gross)
  • ISBN 978-3-642-01088-0
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $239.00
price for USA
  • ISBN 978-3-642-01087-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $239.00
price for USA
  • ISBN 978-3-642-10166-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Foundations of Computational Intelligence
Book Subtitle
Volume 4: Bio-Inspired Data Mining
Editors
  • Ajith Abraham
  • Aboul-Ella Hassanien
  • André Ponce de Leon F. de Carvalho
Series Title
Studies in Computational Intelligence
Series Volume
204
Copyright
2009
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-642-01088-0
DOI
10.1007/978-3-642-01088-0
Hardcover ISBN
978-3-642-01087-3
Softcover ISBN
978-3-642-10166-3
Series ISSN
1860-949X
Edition Number
1
Number of Pages
XIV, 396
Topics