With the recent advances of data generation and acquisition systems and the success of several projects such as Human Genome, a large number of databases, especially in biological field are now available worldwide. The growing rate of such databases is also exponential. There is a need to explore and analyze such massive data to infer some inherent information. Clustering has been recognized as one of the widely used data mining techniques which is essential for data analysis to reveal natural structures and to identify interesting patterns in the underlying data. In the last decade, significant amount of research work has been carried out on cluster analysis and a large number of algorithms have been developed, particularly for biological data. Recently, much attention has been paid to develop various clustering algorithms based on neighborhood graphs such as minimum spanning tree (MST), Voronoi diagram and kd-trees. In this book, we mainly report on the hierarchical, partitional, density-based and graph-based clustering algorithms which are developed using such neighborhood graphs.
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With the recent advances of data generation and acquisition systems and the success of several projects such as Human Genome, a large number of databases, especially in biological field are now available worldwide. The growing rate of such databases is also exponential. There is a need to explore and analyze such massive data to infer some inherent information. Clustering has been recognized as one of the widely used data mining techniques which is essential for data analysis to reveal natural structures and to identify interesting patterns in the underlying data. In the last decade, significant amount of research work has been carried out on cluster analysis and a large number of algorithms have been developed, particularly for biological data. Recently, much attention has been paid to develop various clustering algorithms based on neighborhood graphs such as minimum spanning tree (MST), Voronoi diagram and kd-trees. In this book, we mainly report on the hierarchical, partitional, density-based and graph-based clustering algorithms which are developed using such neighborhood graphs.
Dr. Damodar Reddy Edla is an Asst. Professor in the department of Computer Science & Engineering, National Institute of Technology Goa, India. Dr. Prasanta K. Jana is a Professor and Head of the department of Computer Science & Engineering, Indian School of Mines, Dhanbad, India. Prof. Jana is a Senior Member of IEEE, USA (August 2009-May 2013)
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -With the recent advances of data generation and acquisition systems and the success of several projects such as Human Genome, a large number of databases, especially in biological field are now available worldwide. The growing rate of such databases is also exponential. There is a need to explore and analyze such massive data to infer some inherent information. Clustering has been recognized as one of the widely used data mining techniques which is essential for data analysis to reveal natural structures and to identify interesting patterns in the underlying data. In the last decade, significant amount of research work has been carried out on cluster analysis and a large number of algorithms have been developed, particularly for biological data. Recently, much attention has been paid to develop various clustering algorithms based on neighborhood graphs such as minimum spanning tree (MST), Voronoi diagram and kd-trees. In this book, we mainly report on the hierarchical, partitional, density-based and graph-based clustering algorithms which are developed using such neighborhood graphs. 148 pp. Englisch. N° de réf. du vendeur 9783659454745
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Edla Damodar ReddyDr. Damodar Reddy Edla is an Asst. Professor in the department of Computer Science & Engineering, National Institute of Technology Goa, India. Dr. Prasanta K. Jana is a Professor and Head of the department of Co. N° de réf. du vendeur 5157137
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -With the recent advances of data generation and acquisition systems and the success of several projects such as Human Genome, a large number of databases, especially in biological field are now available worldwide. The growing rate of such databases is also exponential. There is a need to explore and analyze such massive data to infer some inherent information. Clustering has been recognized as one of the widely used data mining techniques which is essential for data analysis to reveal natural structures and to identify interesting patterns in the underlying data. In the last decade, significant amount of research work has been carried out on cluster analysis and a large number of algorithms have been developed, particularly for biological data. Recently, much attention has been paid to develop various clustering algorithms based on neighborhood graphs such as minimum spanning tree (MST), Voronoi diagram and kd-trees. In this book, we mainly report on the hierarchical, partitional, density-based and graph-based clustering algorithms which are developed using such neighborhood graphs.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 148 pp. Englisch. N° de réf. du vendeur 9783659454745
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Vendeur : preigu, Osnabrück, Allemagne
Taschenbuch. Etat : Neu. Clustering Algorithms for Biological Data | Damodar Reddy Edla (u. a.) | Taschenbuch | 148 S. | Englisch | 2013 | LAP LAMBERT Academic Publishing | EAN 9783659454745 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 105618636
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - With the recent advances of data generation and acquisition systems and the success of several projects such as Human Genome, a large number of databases, especially in biological field are now available worldwide. The growing rate of such databases is also exponential. There is a need to explore and analyze such massive data to infer some inherent information. Clustering has been recognized as one of the widely used data mining techniques which is essential for data analysis to reveal natural structures and to identify interesting patterns in the underlying data. In the last decade, significant amount of research work has been carried out on cluster analysis and a large number of algorithms have been developed, particularly for biological data. Recently, much attention has been paid to develop various clustering algorithms based on neighborhood graphs such as minimum spanning tree (MST), Voronoi diagram and kd-trees. In this book, we mainly report on the hierarchical, partitional, density-based and graph-based clustering algorithms which are developed using such neighborhood graphs. N° de réf. du vendeur 9783659454745
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