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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.
Inhaltsverzeichnis
Bio-Inspired Approaches in Sequence and Data Streams.- Adaptive and Self-adaptive Techniques for Evolutionary Forecasting Applications Set in Dynamic and Uncertain Environments.- Sequence Pattern Mining.- Growing Self-Organizing Map for Online Continuous Clustering.- Synthesis of Spatio-temporal Models by the Evolution of Non-uniform Cellular Automata.- Bio-Inspired Approaches in Classification Problem.- Genetic Selection Algorithm and Cloning for Data Mining with GMDH Method.- Inducing Relational Fuzzy Classification Rules by Means of Cooperative Coevolution.- Post-processing Evolved Decision Trees.- Evolutionary Fuzzy and Swarm in Clustering Problems.- Evolutionary Fuzzy Clustering: An Overview and Efficiency Issues.- Stability-Based Model Order Selection for Clustering Using Multiple Cooperative Particle Swarms.- Genetic and Evolutionary Algorithms in Bioinformatics.- Data-Mining Protein Structure by Clustering, Segmentation and Evolutionary Algorithms.- A Clustering Genetic Algorithm for Genomic Data Mining.- Detection of Remote Protein Homologs Using Social Programming.- Bio-Inspired Approaches in Information Retrieval and Visualization.- Optimizing Information Retrieval Using Evolutionary Algorithms and Fuzzy Inference System.- Web Data Clustering.- Efficient Construction of Image Feature Extraction Programs by Using Linear Genetic Programming with Fitness Retrieval and Intermediate-Result Caching.- Mining Network Traffic Data for Attacks through MOVICAB-IDS.
Über den Autor / die Autorin
Dr. Ajith Abraham is Director of the Machine Intelligence Research (MIR) Labs, a global network of research laboratories with headquarters near Seattle, WA, USA. He is an author/co-author of more than 750 scientific publications. He is founding Chair of the International Conference of Computational Aspects of Social Networks (CASoN), Chair of IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing (since 2008), and a Distinguished Lecturer of the IEEE Computer Society representing Europe (since 2011).
Dr. Aboul-Ella Hassanien is a Professor in the Faculty of Computers and Information at Cairo University, Egypt, and Visiting Professor at the College of Business Administration, Kuwait University.
Dr. Aboul-Ella Hassanien is a Professor in the Faculty of Computers and Information at Cairo University, Egypt, and Visiting Professor at the College of Business Administration, Kuwait University.
Zusammenfassung
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.
