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

Lays Helena Lopes Veloso

ISBN 10: 6209114083 / ISBN 13: 9786209114083

Published by Our Knowledge Publishing, 2025

About this Item

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.

Seller Inventory # 9786209114083

Bibliographic Details

• Title: Parallel K-Means Algorithm based on Hadoop-MapReduce for Mining (Paperback)
• Publisher: Our Knowledge Publishing
• Publication Date: 2025
• Language: English
• ISBN 10: 6209114083
• ISBN 13: 9786209114083
• Binding: Paperback
• Condition: new

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.