Foundations of Computational Intelligence

1. Front Matter

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2. Bio-Inspired Approaches in Sequence and Data Streams

   1. Front Matter

      Pages 1-1

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

      • Neal Wagner, Zbigniew Michalewicz

      Pages 3-21

   3. Sequence Pattern Mining

      • Huiyu Zhou, Kaoru Shimada, Shingo Mabu, Kotaro Hirasawa

      Pages 23-48

   4. Growing Self-Organizing Map for Online Continuous Clustering

      • Toby Smith, Damminda Alahakoon

      Pages 49-83

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

      • 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

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   2. Genetic Selection Algorithm and Cloning for Data Mining with GMDH Method

      • Marcel Jirina, Marcel Jirina Jr.

      Pages 107-125

   3. Inducing Relational Fuzzy Classification Rules by Means of Cooperative Coevolution

      • Vahab Akbarzadeh, Alireza Sadeghian, Marcus V. dos Santos

      Pages 127-147

   4. Post-processing Evolved Decision Trees

      • 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

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   2. Evolutionary Fuzzy Clustering: An Overview and Efficiency Issues

      • D. Horta, M. Naldi, R. J. G. B. Campello, E. R. Hruschka, A. C. P. L. F. de Carvalho

      Pages 167-195

   3. Stability-Based Model Order Selection for Clustering Using Multiple Cooperative Particle Swarms

      • Abbas Ahmadi, Fakhri Karray, Mohamed S. Kamel

      Pages 197-218

5. Genetic and Evolutionary Algorithms in Bioinformatics

   1. Front Matter

      Pages 219-219

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   2. Data-Mining Protein Structure by Clustering, Segmentation and Evolutionary Algorithms

      • Matej Lexa, Václav Snášel, Ivan Zelinka

      Pages 221-248

   3. A Clustering Genetic Algorithm for Genomic Data Mining

      • José Juan Tapia, Enrique Morett, Edgar E. Vallejo

      Pages 249-275

   4. Detection of Remote Protein Homologs Using Social Programming

      • Gerard Ramstein, Nicolas Beaume, Yannick Jacques

      Pages 277-296

6. Bio-Inspired Approaches in Information Retrieval and Visualization

   1. Front Matter

      Pages 297-297

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   2. Optimizing Information Retrieval Using Evolutionary Algorithms and Fuzzy Inference System

      • Václav Snášel, Ajith Abraham, Suhail Owais, Jan Platoš, Pavel Krömer

      Pages 299-324

   3. Web Data Clustering

      • Dušan Húsek, Jaroslav Pokorný, Hana Řezanková, Václav Snášel

      Pages 325-353

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.

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

• Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn, Washington, USA

  Ajith Abraham

• College of Business Administration, Quantitative and Information System Department, Kuwait University, Safat, Kuwait

  Aboul-Ella Hassanien

• Department of Computer Science, University of São Paulo,SCE - ICMSC - USP, Sao Carlos, Brazil

  André Ponce de Leon F. Carvalho

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