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Computer Science - Artificial Intelligence | Multiobjective Genetic Algorithms for Clustering - Applications in Data Mining and Bioinformatics

Multiobjective Genetic Algorithms for Clustering

Applications in Data Mining and Bioinformatics

Maulik, Ujjwal, Bandyopadhyay, Sanghamitra, Mukhopadhyay, Anirban

2011, XVI, 281 p.

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  • Introduces relevant techniques including genetic algorithms, multiobjective optimization, soft computing and data mining
  • Demonstrates application of these techniques to real-world problems in computational biology, bioinformatics and geoscience
  • Includes theoretical and statistical notes, chapter summaries and guides to future research

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.

Content Level » Graduate

Keywords » Bioinformatics - Clustering - Computational Biology - Data Mining - Genetic Algorithms - Geoscience - Multiobjective Optimization - Pattern Recognition - Remote Sensing - Soft Computing - Supervised Learning

Related subjects » Artificial Intelligence - Bioinformatics - Computational Intelligence and Complexity - Database Management & Information Retrieval

Table of contents 

Introduction.- Genetic Algorithms and Multiobjective Optimization.- Data Mining Fundamentals.- Computational Biology and Bioinformatics.- Multiobjective Genetic-Algorithm-Based Fuzzy Clustering.- Combining Pareto-Optimal Clusters Using Supervised Learning.- Two-Stage Fuzzy Clustering.- Clustering Categorical Data in a Multiobjective Framework.- Unsupervised Cancer Classification and Gene Marker Identification.- Multiobjective Biclustering in Microarray Gene Expression Data.- References.- Index.

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