Foundations of Computational Intelligence Volume 4

Bio-Inspired Data Mining

  • Ajith Abraham
  • Aboul-Ella Hassanien
  • André Ponce de Leon F. de Carvalho

Part of the Studies in Computational Intelligence book series (SCI, volume 204)

Table of contents

  1. Front Matter
  2. Bio-Inspired Approaches in Sequence and Data Streams

    1. Front Matter
      Pages 1-1
    2. Huiyu Zhou, Kaoru Shimada, Shingo Mabu, Kotaro Hirasawa
      Pages 23-48
    3. Toby Smith, Damminda Alahakoon
      Pages 49-83
    4. Ana L. T. Romano, Wilfredo J. P. Villanueva, Marcelo S. Zanetti, Fernando J. Von Zuben
      Pages 85-104
  3. Bio-Inspired Approaches in Classification Problem

    1. Front Matter
      Pages 105-105
    2. Marcel Jirina, Marcel Jirina Jr.
      Pages 107-125
    3. Vahab Akbarzadeh, Alireza Sadeghian, Marcus V. dos Santos
      Pages 127-147
    4. Ulf Johansson, Rikard König, Tuve Löfström, Cecilia Sönströd, Lars Niklasson
      Pages 149-164
  4. Evolutionary Fuzzy and Swarm in Clustering Problems

    1. Front Matter
      Pages 165-165
    2. D. Horta, M. Naldi, R. J. G. B. Campello, E. R. Hruschka, A. C. P. L. F. de Carvalho
      Pages 167-195
  5. Genetic and Evolutionary Algorithms in Bioinformatics

    1. Front Matter
      Pages 219-219
    2. Matej Lexa, Václav Snášel, Ivan Zelinka
      Pages 221-248
    3. José Juan Tapia, Enrique Morett, Edgar E. Vallejo
      Pages 249-275
    4. Gerard Ramstein, Nicolas Beaume, Yannick Jacques
      Pages 277-296
  6. Bio-Inspired Approaches in Information Retrieval and Visualization

    1. Front Matter
      Pages 297-297
    2. Václav Snášel, Ajith Abraham, Suhail Owais, Jan Platoš, Pavel Krömer
      Pages 299-324
    3. Dušan Húsek, Jaroslav Pokorný, Hana Řezanková, Václav Snášel
      Pages 325-353
    4. Ukrit Watchareeruetai, Tetsuya Matsumoto, Yoshinori Takeuchi, Hiroaki Kudo, Noboru Ohnishi
      Pages 355-375
    5. Álvaro Herrero, Emilio Corchado
      Pages 377-394
  7. Back Matter

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.


algorithms bioinformatics classification clustering computational intelligence data mining evolutionary algorithm fuzzy genetic algorithms genetic programming intelligence model pattern mining programming visualization

Editors and affiliations

  • Ajith Abraham
    • 1
  • Aboul-Ella Hassanien
    • 2
  • André Ponce de Leon F. de Carvalho
    • 3
  1. 1.Machine Intelligence Research Labs (MIR Labs)Scientific Network for Innovation and Research ExcellenceAuburn, WashingtonUSA
  2. 2.College of Business Administration, Quantitative and Information System DepartmentKuwait UniversitySafatKuwait
  3. 3.Department of Computer ScienceUniversity of São Paulo,SCE - ICMSC - USPSao CarlosBrazil

Bibliographic information

  • DOI
  • Copyright Information Springer Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-642-01087-3
  • Online ISBN 978-3-642-01088-0
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
Industry Sectors
IT & Software
Oil, Gas & Geosciences