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Metaheuristic Clustering - Swagatam Das, Amit Konar, Ajith Abraham
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Metaheuristic Clustering - used book

ISBN: 9783642100710

ID: 9783642100710

Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity. The task of computerized data clustering has been approached from diverse domains of knowledge like graph theory, multivariate analysis, neural networks, fuzzy set theory, and so on. Clustering is often described as an unsupervised learning method but most of the traditional algorithms require a prior specification of the number of clusters in the data for Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity. The task of computerized data clustering has been approached from diverse domains of knowledge like graph theory, multivariate analysis, neural networks, fuzzy set theory, and so on. Clustering is often described as an unsupervised learning method but most of the traditional algorithms require a prior specification of the number of clusters in the data for guiding the partitioning process, thus making it not completely unsupervised. Modern data mining tools that predict future trends and behaviors for allowing businesses to make proactive and knowledge-driven decisions, demand fast and fully automatic clustering of very large datasets with minimal or no user intervention. In this volume, we formulate clustering as an optimization problem, where the best partitioning of a given dataset is achieved by minimizing/maximizing one (single-objective clustering) or more (multi-objective clustering) objective functions. Using several real world applications, we illustrate the performance of several metaheuristics, particularly the Differential Evolution algorithm when applied to both single and multi-objective clustering problems, where the number of clusters is not known beforehand and must be determined on the run. This volume comprises of 7 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges. Academics, scientists as well as engineers engaged in research, development and application of optimization techniques and data mining will find the comprehensive coverage of this book invaluable. Books, Mathematics~~Applied, Metaheuristic-Clustering~~Swagatam-Das, 999999999, Metaheuristic Clustering, Swagatam Das, Amit Konar, Ajith Abraham, 3642100716, Springer Berlin Heidelberg, , , , , Springer Berlin Heidelberg

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Metaheuristic Clustering
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Metaheuristic Clustering - new book

ISBN: 9783642100710

ID: 11098843

Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity. The task of computerized data clustering has been approached from diverse domains of knowledge like graph theory, multivariate analysis, neural networks, fuzzy set theory, and so on. Clustering is often described as an unsupervised learning method but most of Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity. The task of computerized data clustering has been approached from diverse domains of knowledge like graph theory, multivariate analysis, neural networks, fuzzy set theory, and so on. Clustering is often described as an unsupervised learning method but most of the traditional algorithms require a prior specification of the number of clusters in the data for guiding the partitioning process, thus making it not completely unsupervised. Modern data mining tools that predict future trends and behaviors for allowing businesses to make proactive and knowledge-driven decisions, demand fast and fully automatic clustering of very large datasets with minimal or no user intervention. In this volume, we formulate clustering as an optimization problem, where the best partitioning of a given dataset is achieved by minimizing/maximizing one (single-objective clustering) or more (multi-objective clustering) objective functions. Using several real world applications, we illustrate the performance of several metaheuristics, particularly the Differential Evolution algorithm when applied to both single and multi-objective clustering problems, where the number of clusters is not known beforehand and must be determined on the run. This volume comprises of 7 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges. Academics, scientists as well as engineers engaged in research, development and application of optimization techniques and data mining will find the comprehensive coverage of this book invaluable. Books, Technology, Engineering and Agriculture~~Technology: general issues~~Maths for Engineers, Metaheuristic Clustering~~Book~~9783642100710~~Amit Konar, Ajith Abraham, Swagatam Das, , , , , , , , , ,, [PU: Springer, Berlin/Heidelberg/New York, NY]

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Abraham, Ajith;Das, Swagatam;Konar, Amit: Metaheuristic Clustering
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Abraham, Ajith;Das, Swagatam;Konar, Amit: Metaheuristic Clustering - new book

2009, ISBN: 9783642100710

ID: 681838268

Studies in Computational Intelligence. Softcover reprint of hardcover 1st ed. 2009. Studies in Computational Intelligence. Softcover reprint of hardcover 1st ed. 2009. Bücher > English, International > Gebundene Ausgaben, [PU: Springer, Berlin/Heidelberg/New York, NY]

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Metaheuristic Clustering - Swagatam Das; Ajith Abraham; Amit Konar
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2010, ISBN: 9783642100710

Paperback, Hardcover, ID: 18274738

[ED: 1], 1., Softcover reprint of hardcover 1st ed. 2009, Softcover, Buch, [PU: Springer Berlin]

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Metaheuristic Clustering - Swagatam Das; Ajith Abraham; Amit Konar
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Swagatam Das; Ajith Abraham; Amit Konar:
Metaheuristic Clustering - Paperback

2010, ISBN: 9783642100710

Hardcover, ID: 18274738

Softcover reprint of hardcover 1st ed. 2009, Softcover, Buch, [PU: Springer Berlin]

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Details of the book
Metaheuristic Clustering

Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity. The task of computerized data clustering has been approached from diverse domains of knowledge like graph theory, multivariate analysis, neural networks, fuzzy set theory, and so on. Clustering is often described as an unsupervised learning method but most of the traditional algorithms require a prior specification of the number of clusters in the data for guiding the partitioning process, thus making it not completely unsupervised. Modern data mining tools that predict future trends and behaviors for allowing businesses to make proactive and knowledge-driven decisions, demand fast and fully automatic clustering of very large datasets with minimal or no user intervention. In this volume, we formulate clustering as an optimization problem, where the best partitioning of a given dataset is achieved by minimizing/maximizing one (single-objective clustering) or more (multi-objective clustering) objective functions. Using several real world applications, we illustrate the performance of several metaheuristics, particularly the Differential Evolution algorithm when applied to both single and multi-objective clustering problems, where the number of clusters is not known beforehand and must be determined on the run. This volume comprises of 7 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges.Academics, scientists as well as engineers engaged in research, development and application of optimization techniques and data mining will find the comprehensive coverage of this book invaluable.

Details of the book - Metaheuristic Clustering


EAN (ISBN-13): 9783642100710
ISBN (ISBN-10): 3642100716
Hardcover
Paperback
Publishing year: 2010
Publisher: Springer-Verlag GmbH
272 Pages
Weight: 0,415 kg
Language: eng/Englisch

Book in our database since 24.01.2011 03:46:41
Book found last time on 13.06.2017 11:47:16
ISBN/EAN: 9783642100710

ISBN - alternate spelling:
3-642-10071-6, 978-3-642-10071-0


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