Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This work aimed to investigate the use of a parallel K-Means clustering algorithm, based on the MapReduce programming model, to improve the response time of data mining. The algorithm's performance was evaluated in terms of SpeedUp and ScaleUp. To this end, experiments were performed on a Hadoop cluster consisting of six computers with standard hardware. The clustered data are measurements from flow towers in agricultural regions and belong to Ameriflux. The experiments were performed using 3, 4, and 6 machines, respectively. The results showed that with the increase in the number of machines, there was a gain in performance, with the best time obtained using six machines, reaching a SpeedUp of 3.25. It was found that the application scales well with the equivalent increase in data size and number of machines in the cluster, achieving similar performance in the tests. 56 pp. Englisch. N� de r�f. du vendeur 9786209114083

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Parallel K-Means Algorithm based on Hadoop-MapReduce for Mining

Lopes Veloso, Lays Helena; Senger, Luciano Jos�

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ISBN 10 : 6209114083 ISBN 13 : 9786209114083

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Parallel K-Means Algorithm based on Hadoop-MapReduce for Mining

Lopes Veloso, Lays Helena; Senger, Luciano Jos�

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ISBN 10 : 6209114083 ISBN 13 : 9786209114083

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Parallel K-Means Algorithm based on Hadoop-MapReduce for Mining

Lopes Veloso, Lays Helena; Senger, Luciano Jos�

Edit� par Our Knowledge Publishing, 2025

ISBN 10 : 6209114083 ISBN 13 : 9786209114083

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Parallel K-Means Algorithm based on Hadoop-MapReduce for Mining (Paperback)

Lays Helena Lopes Veloso

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Paperback. Etat : new. Paperback. This work aimed to investigate the use of a parallel K-Means clustering algorithm, based on the MapReduce programming model, to improve the response time of data mining. The algorithm's performance was evaluated in terms of SpeedUp and ScaleUp. To this end, experiments were performed on a Hadoop cluster consisting of six computers with standard hardware. The clustered data are measurements from flow towers in agricultural regions and belong to Ameriflux. The experiments were performed using 3, 4, and 6 machines, respectively. The results showed that with the increase in the number of machines, there was a gain in performance, with the best time obtained using six machines, reaching a SpeedUp of 3.25. It was found that the application scales well with the equivalent increase in data size and number of machines in the cluster, achieving similar performance in the tests. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N� de r�f. du vendeur 9786209114083

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Parallel K-Means Algorithm based on Hadoop-MapReduce for Mining

Lays Helena Lopes Veloso

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ISBN 10 : 6209114083 ISBN 13 : 9786209114083

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This work aimed to investigate the use of a parallel K-Means clustering algorithm, based on the MapReduce programming model, to improve the response time of data mining. The algorithm's performance was evaluated in terms of SpeedUp and ScaleUp. To this end, experiments were performed on a Hadoop cluster consisting of six computers with standard hardware. The clustered data are measurements from flow towers in agricultural regions and belong to Ameriflux. The experiments were performed using 3, 4, and 6 machines, respectively. The results showed that with the increase in the number of machines, there was a gain in performance, with the best time obtained using six machines, reaching a SpeedUp of 3.25. It was found that the application scales well with the equivalent increase in data size and number of machines in the cluster, achieving similar performance in the tests.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 56 pp. Englisch. N� de r�f. du vendeur 9786209114083

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Parallel K-Means Algorithm based on Hadoop-MapReduce for Mining

Lays Helena Lopes Veloso

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ISBN 10 : 6209114083 ISBN 13 : 9786209114083

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This work aimed to investigate the use of a parallel K-Means clustering algorithm, based on the MapReduce programming model, to improve the response time of data mining. The algorithm's performance was evaluated in terms of SpeedUp and ScaleUp. To this end, experiments were performed on a Hadoop cluster consisting of six computers with standard hardware. The clustered data are measurements from flow towers in agricultural regions and belong to Ameriflux. The experiments were performed using 3, 4, and 6 machines, respectively. The results showed that with the increase in the number of machines, there was a gain in performance, with the best time obtained using six machines, reaching a SpeedUp of 3.25. It was found that the application scales well with the equivalent increase in data size and number of machines in the cluster, achieving similar performance in the tests. N� de r�f. du vendeur 9786209114083

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Parallel K-Means Algorithm based on Hadoop-MapReduce for Mining

Lays Helena Lopes Veloso (u. a.)

Edit� par Our Knowledge Publishing, 2025

ISBN 10 : 6209114083 ISBN 13 : 9786209114083

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Taschenbuch. Etat : Neu. Parallel K-Means Algorithm based on Hadoop-MapReduce for Mining | Lays Helena Lopes Veloso (u. a.) | Taschenbuch | Englisch | 2025 | Our Knowledge Publishing | EAN 9786209114083 | Verantwortliche Person f�r die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. N� de r�f. du vendeur 134142141

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