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

Lays Helena Lopes Veloso (u. a.)

ISBN 10: 6209114083 / ISBN 13: 9786209114083

Published by Our Knowledge Publishing, 2025

About this Item

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.

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Bibliographic Details

• Title: Parallel K-Means Algorithm based on Hadoop-MapReduce for Mining
• Publisher: Our Knowledge Publishing
• Publication Date: 2025
• Language: English
• ISBN 10: 6209114083
• ISBN 13: 9786209114083
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 4D millimeters 220 millimeters width by 150 millimeters height by 4 millimeters depth
• Weight: 102 grams
• Seller catalogs:
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About this title

Synopsis

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.