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Improving Efficiency of K-Means Algorithm for Large Datasets

Improving Efficiency of K-Means Algorithm for Large Datasets

Ch. Swetha Swapna, V. Vijaya Kumar, J.V.R Murthy
Copyright: © 2016 |Volume: 3 |Issue: 2 |Pages: 9
ISSN: 2334-4598|EISSN: 2334-4601|EISBN13: 9781466694019|DOI: 10.4018/IJRSDA.2016040101
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MLA

Swapna, Ch. Swetha, et al. "Improving Efficiency of K-Means Algorithm for Large Datasets." IJRSDA vol.3, no.2 2016: pp.1-9. http://doi.org/10.4018/IJRSDA.2016040101

APA

Swapna, C. S., Kumar, V. V., & Murthy, J. (2016). Improving Efficiency of K-Means Algorithm for Large Datasets. International Journal of Rough Sets and Data Analysis (IJRSDA), 3(2), 1-9. http://doi.org/10.4018/IJRSDA.2016040101

Chicago

Swapna, Ch. Swetha, V. Vijaya Kumar, and J.V.R Murthy. "Improving Efficiency of K-Means Algorithm for Large Datasets," International Journal of Rough Sets and Data Analysis (IJRSDA) 3, no.2: 1-9. http://doi.org/10.4018/IJRSDA.2016040101

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Abstract

Clustering is a process of grouping objects into different classes based on their similarities. K-means is a widely studied partitional based algorithm. It is reported to work efficiently for small datasets; however the performance is not very appreciable in terms of time of computation for large datasets. Several modifications have been made by researchers to address this issue. This paper proposes a novel way of handling the large datasets using K-means in a distributed manner to obtain efficiency. The concept of parallel processing is exploited by dividing the datasets to a number of baskets and then applying K-means in parallel manner to each such basket. The proposed BasketK-means provides a very competitive performance with considerably less computation time. The simulation results on various real datasets and synthetic datasets presented in the work clearly emphasize the effectiveness of the proposed approach.

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