Overview
- Editors:
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Ajith Abraham
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Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn, Washington, USA
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Aboul-Ella Hassanien
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College of Business Administration, Quantitative and Information System Department, Kuwait University, Safat, Kuwait
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André Ponce de Leon F. Carvalho
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Department of Computer Science, University of São Paulo,SCE - ICMSC - USP, Sao Carlos, Brazil
- Fourth volume of a Reference work on the foundations of Computational Intelligence
- Devoted to bio-inspired data mining
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Table of contents (16 chapters)
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Bio-Inspired Approaches in Sequence and Data Streams
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- Neal Wagner, Zbigniew Michalewicz
Pages 3-21
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- Huiyu Zhou, Kaoru Shimada, Shingo Mabu, Kotaro Hirasawa
Pages 23-48
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- Toby Smith, Damminda Alahakoon
Pages 49-83
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- Ana L. T. Romano, Wilfredo J. P. Villanueva, Marcelo S. Zanetti, Fernando J. Von Zuben
Pages 85-104
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Bio-Inspired Approaches in Classification Problem
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Front Matter
Pages 105-105
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- Marcel Jirina, Marcel Jirina Jr.
Pages 107-125
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- Vahab Akbarzadeh, Alireza Sadeghian, Marcus V. dos Santos
Pages 127-147
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- Ulf Johansson, Rikard König, Tuve Löfström, Cecilia Sönströd, Lars Niklasson
Pages 149-164
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Evolutionary Fuzzy and Swarm in Clustering Problems
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Front Matter
Pages 165-165
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- D. Horta, M. Naldi, R. J. G. B. Campello, E. R. Hruschka, A. C. P. L. F. de Carvalho
Pages 167-195
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- Abbas Ahmadi, Fakhri Karray, Mohamed S. Kamel
Pages 197-218
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Genetic and Evolutionary Algorithms in Bioinformatics
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Front Matter
Pages 219-219
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- Matej Lexa, Václav Snášel, Ivan Zelinka
Pages 221-248
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- José Juan Tapia, Enrique Morett, Edgar E. Vallejo
Pages 249-275
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- Gerard Ramstein, Nicolas Beaume, Yannick Jacques
Pages 277-296
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Bio-Inspired Approaches in Information Retrieval and Visualization
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Front Matter
Pages 297-297
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- Václav Snášel, Ajith Abraham, Suhail Owais, Jan Platoš, Pavel Krömer
Pages 299-324
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- Dušan Húsek, Jaroslav Pokorný, Hana Řezanková, Václav Snášel
Pages 325-353
About this book
Foundations of Computational Intelligence Volume 4: Bio-Inspired Data Mining Theoretical Foundations and Applications 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 mo- toring, 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 - phisticated 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 parti- lar hypothesis or to automatically find patterns. In the second case, which is - lated 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 p- dict 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.