Foundations of Computational Intelligence Volume 3: Global Optimization / Edition 1

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More About This Textbook

Overview

Finding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived from statistics, machine learning, and artificial intelligence, and are being used successfully in application areas such as bioinformatics, business, health care, banking, retail, and many others. Advanced representation schemes and computational intelligence techniques such as rough sets, neural networks; decision trees; fuzzy logic; evolutionary algorithms; artificial immune systems; swarm intelligence; reinforcement learning, association rule mining, Web intelligence paradigms etc. have proved valuable when they are applied to Data Mining problems. Computational tools or solutions based on intelligent systems are being used with great success in Data Mining applications. It is also observed that strong scientific advances have been made when issues from different research areas are integrated.

This Volume comprises of 15 chapters including an overview chapter providing an up-to-date and state-of-the research on the applications of Computational Intelligence techniques for Data Mining.

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Product Details

  • ISBN-13: 9783642010842
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 4/23/2009
  • Series: Studies in Computational Intelligence Series , #203
  • Edition description: 2009
  • Edition number: 1
  • Pages: 528
  • Product dimensions: 6.40 (w) x 9.30 (h) x 1.40 (d)

Table of Contents

Part-I: 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: Genetic Network Programming Approach.- Growing Self-Organizing Map for Online Continuous Clustering.- Synthesis of Spatio-Temporal Models by the Evolution of Non-Uniform Cellular Automata.- Part-II 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 .- Part-III: 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 Swarms .- 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.- Part-V: 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.

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