Thesis report

Parallel K-Means Clustering using Hadoop MapReduce

5. COMPARATIVE ANALYSIS: 5.1.Experimental results of Execution Time Analysis for K- Means Clustering Algorithm Execution time analysis for K-means clustering algorithm is done on the basis of the number of records that are considered for clustering and how much time is taken by this whole process. As if the number of records are 100 that are considered for clustering, then it takes execution time 132ms and so on for all records. So in this way using different number of records, the execution time differentiation is shown.

Fig 5.1 Execution time of K-means clustering algorithm. In the table that also shows the number of records and the clustering execution time taken by K-means clustering algorithm is shown. As if the number of records are 50, the the execution time will be 98ms and so on. With the help of this type of tables we can easily calculate the performance.

Computer Engineering Department, DJSCOE

Page 23

Thesis report

Published on May 16, 2014

Programming

Sagar Makwana

About

Data clustering has been received considerable attention in many applications, such as data mining, document retrieval, image segmentation and pattern classification. The enlarging volumes of information emerging by the progress of technology, makes clustering of very large scale of data a challenging task. In order to deal with the problem, many researchers try to design efficient parallel clustering algorithms. In this paper, we propose a parallel k-means clustering algorithm based on MapReduce, which is a simple yet powerful parallel programming technique. The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware. The results also show that the MapReduce framework K-Means clustering algorithm can obtain a higher performance when handling largescale document automatic classification, and they prove the effectiveness and accuracy of the algorithm.

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