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

• Κύριοι συγγραφείς: Maulik, Ujjwal (Συγγραφέας), Bandyopadhyay, Sanghamitra (Συγγραφέας), Mukhopadhyay, Anirban (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011.
• Θέματα: Computer science.; Data mining.; Artificial intelligence.; Bioinformatics.; Computational intelligence.; Computer Science.; Artificial Intelligence (incl. Robotics).; Computational Biology/Bioinformatics.; Data Mining and Knowledge Discovery.; Computational Intelligence.

 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. 

 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.