Multiobjective Genetic Algorithms for Clustering: Applications in Data Mining and Bioinformatics

✍ Scribed by Ujjwal Maulik, Sanghamitra Bandyopadhyay, Anirban Mukhopadhyay (auth.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2011
• Tongue: English
• Leaves: 298
• Edition: 1
• Category: Library

✦ Synopsis

This is the first book primarily dedicated to clustering using multiobjective genetic algorithms with extensive real-life applications in data mining and bioinformatics. The authors first offer detailed introductions to the relevant techniques – genetic algorithms, multiobjective optimization, soft computing, data mining and bioinformatics. They then demonstrate systematic applications of these techniques to real-world problems in the areas of data mining, bioinformatics and geoscience. The authors offer detailed theoretical and statistical notes, guides to future research, and chapter summaries.

The book can be used as a textbook and as a reference book by graduate students and academic and industrial researchers in the areas of soft computing, data mining, bioinformatics and geoscience.

✦ Table of Contents

Front Matter....Pages i-xvi
Introduction....Pages 1-23
Genetic Algorithms and Multiobjective Optimization....Pages 25-50
Data Mining Fundamentals....Pages 51-70
Computational Biology and Bioinformatics....Pages 71-88
Multiobjective Genetic Algorithm-Based Fuzzy Clustering....Pages 89-121
Combining Pareto-Optimal Clusters Using Supervised Learning....Pages 123-145
Two-Stage Fuzzy Clustering....Pages 147-171
Clustering Categorical Data in a Multiobjective Framework....Pages 173-194
Unsupervised Cancer Classification and Gene Marker Identification....Pages 195-212
Multiobjective Biclustering in Microarray Gene Expression Data....Pages 213-253
Back Matter....Pages 255-281

✦ Subjects

Artificial Intelligence (incl. Robotics); Computational Biology/Bioinformatics; Data Mining and Knowledge Discovery; Computational Intelligence