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Parallel K-Means Algorithm based on Hadoop-MapReduce for Mining
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Parallel K-Means Algorithm based on Hadoop-MapReduce for Mining
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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Codice articolo L2-9786209114083
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Parallel K-Means Algorithm based on Hadoop-MapReduce for Mining
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Omniscriptum, Our Knowledge Publishing Okt 2025, 2025
ISBN 10: 6209114083
ISBN 13: 9786209114083
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: 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. Codice articolo 9786209114083
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Parallel K-Means Algorithm based on Hadoop-MapReduce for Mining
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Parallel K-Means Algorithm based on Hadoop-MapReduce for Mining
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Condizione: New. Print on Demand. Codice articolo 408114098
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Parallel K-Means Algorithm based on Hadoop-MapReduce for Mining
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Parallel K-Means Algorithm based on Hadoop-MapReduce for Mining (Paperback)
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Paperback. Condizione: 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. Codice articolo 9786209114083
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Parallel K-Means Algorithm based on Hadoop-MapReduce for Mining
Editore:
Our Knowledge Publishing Okt 2025, 2025
ISBN 10: 6209114083
ISBN 13: 9786209114083
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: 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 Landstraße 99, 66123 Saarbrücken 56 pp. Englisch. Codice articolo 9786209114083
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Parallel K-Means Algorithm based on Hadoop-MapReduce for Mining
Editore:
Our Knowledge Publishing, 2025
ISBN 10: 6209114083
ISBN 13: 9786209114083
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: 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. Codice articolo 9786209114083
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Parallel K-Means Algorithm based on Hadoop-MapReduce for Mining
Editore:
Our Knowledge Publishing, 2025
ISBN 10: 6209114083
ISBN 13: 9786209114083
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Da: preigu, Osnabrück, Germania
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Taschenbuch. Condizione: 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 für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Codice articolo 134142141
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