Multiobjective Genetic Algorithms for Clustering
Applications in Data Mining and Bioinformatics
(Sprache: Englisch)
This book covers clustering using multiobjective genetic algorithms, with extensive real-life application in data mining and bioinformatics. The authors offer instructions for relevant techniques, and demonstrate real-world applications in several disciplines.
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This book covers clustering using multiobjective genetic algorithms, with extensive real-life application in data mining and bioinformatics. The authors offer instructions for relevant techniques, and demonstrate real-world applications in several disciplines.
Klappentext zu „Multiobjective Genetic Algorithms for Clustering “
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.
Inhaltsverzeichnis zu „Multiobjective Genetic Algorithms for Clustering “
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.
Autoren-Porträt von Ujjwal Maulik, Sanghamitra Bandyopadhyay, Anirban Mukhopadhyay
Prof. Sanghamitra Bandyopadhyay has many years of experience in the development of soft computing techniques. Among other awards and positions, she has received senior researcher Humboldt Fellowships, and she is a regular visitor to the DKFZ (German Cancer Research Centre) and to European and North American universities, collaborating in multidisciplinary teams on applications in the areas of computational biology and bioinformatics. Among other awards Prof. Bandyopadhyay received the prestigious Shanti Swarup Bhatnagar Prize in Engineering Sciences in 2010, she is a Fellow of the National Academy of Sciences of India and she is a Fellow of the Indian National Academy of Engineering. Dr. Sriparna Saha is an assistant professor in the Indian Institute of Technology Patna. Among her positions and awards, she was a postdoctoral researcher in Trento and in Heidelberg, and she received the Google India Women in Engineering Award in 2008. Her research interests include multiobjective optimization, evolutionary computation, clustering, and pattern recognition.
Bibliographische Angaben
- Autoren: Ujjwal Maulik , Sanghamitra Bandyopadhyay , Anirban Mukhopadhyay
- 2010, XVI, 281 Seiten, Masse: 16 x 24,1 cm, Gebunden, Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3642166148
- ISBN-13: 9783642166143
- Erscheinungsdatum: 02.09.2011
Sprache:
Englisch
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