Das, Swagatam;Abraham, Ajith;Konar, Amit:
Metaheuristic Clustering - hardcover
2009, ISBN: 9783540921721
[ED: Hardcover], [PU: Springer / Springer Berlin Heidelberg / Springer, Berlin], Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate gr… More...
[ED: Hardcover], [PU: Springer / Springer Berlin Heidelberg / Springer, Berlin], 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.
2009. xviii, 252 S. XVIII, 252 p. 235 mm
Versandfertig in 6-10 Tagen, DE, [SC: 0.00], Neuware, gewerbliches Angebot, Offene Rechnung (Vorkasse vorbehalten)<
| | booklooker.debuecher.de GmbH & Co. KG Shipping costs:Versandkostenfrei, Versand nach Deutschland. (EUR 0.00) Details... |
(*) Book out-of-stock means that the book is currently not available at any of the associated platforms we search.
Das, Swagatam:
Metaheuristic Clustering / Swagatam Das (u. a.) / Buch / Studies in Computational Intelligence / Englisch / 2009 / Springer Berlin / EAN 9783540921721 - hardcover
2009, ISBN: 9783540921721
[ED: Gebunden], [PU: Springer Berlin], Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity. The task o… More...
[ED: Gebunden], [PU: Springer Berlin], 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., DE, [SC: 0.00], Neuware, gewerbliches Angebot, 254, [GW: 545g], Banküberweisung, PayPal, Sofortüberweisung<
| | booklooker.deBuchbär Shipping costs:Versandkostenfrei, Versand nach Deutschland. (EUR 0.00) Details... |
(*) Book out-of-stock means that the book is currently not available at any of the associated platforms we search.
Das, Swagatam:
Metaheuristic Clustering Swagatam Das (u. a.) Buch Studies in Computational Intelligence Englisch 2009 Springer Berlin EAN 9783540921721 - hardcover
2009, ISBN: 9783540921721
[ED: Gebunden], [PU: Springer Berlin], Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity. The task o… More...
[ED: Gebunden], [PU: Springer Berlin], 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., DE, [SC: 0.00], Neuware, gewerbliches Angebot, 254, [GW: 545g], Banküberweisung, PayPal, Sofortüberweisung<
| | booklooker.depreigu Shipping costs:Versandkostenfrei, Versand nach Deutschland. (EUR 0.00) Details... |
(*) Book out-of-stock means that the book is currently not available at any of the associated platforms we search.
Swagatam Das:
Metaheuristic Clustering - new book
2024, ISBN: 9783540921721
[ED: Buch], [PU: Springer-Verlag GmbH], Neuware - Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity.… More...
[ED: Buch], [PU: Springer-Verlag GmbH], Neuware - 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., DE, [SC: 0.00], Neuware, gewerbliches Angebot, 244x166x23 mm, 254, [GW: 545g], Banküberweisung, PayPal, Offene Rechnung (Vorkasse vorbehalten)<
| | booklooker.deBuchhandlung - Bides GbR Shipping costs:Versandkostenfrei, Versand nach Deutschland. (EUR 0.00) Details... |
(*) Book out-of-stock means that the book is currently not available at any of the associated platforms we search.
Swagatam Das, Ajith Abraham, Amit Konar:
Metaheuristic Clustering - new book
2009, ISBN: 3540921729
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… More...
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. Media Buch, 252 Seiten, Media > Books, Springer Berlin Heidelberg, 2009<
| | Weltbild.deNr. 19093418. Shipping costs:, 2-5 Werktage, zzgl. Versandkosten. (EUR 8.95) Details... |
(*) Book out-of-stock means that the book is currently not available at any of the associated platforms we search.