Foundations of Computational Intelligence : Volume 4: Bio-Inspired Data Mining /
| Corporate Author: | |
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| Other Authors: | , , |
| Format: | eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2009.
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| Edition: | 1st ed. 2009. |
| Series: | Studies in Computational Intelligence,
204 |
| Subjects: |
Table of Contents:
- 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.