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Title: | Parallel Framework for Efficient k-means Clustering |
Authors: | Goyal, Navneet Goyal, Poonam |
Keywords: | Computer Science Clustering Clustering framework Parallel framework |
Issue Date: | Oct-2015 |
Publisher: | ACM Digital Library |
Abstract: | Handling and processing of larger volume of data requires efficient data mining algorithms. k-means is a very popular clustering algorithm for data mining, but its performance suffers because of initial seeding problem. The computation time of k-means algorithm is directly proportional to the number of data-points, number of dimensions, and number of iterations, therefore, it is very expensive to process large data-points sequentially. We proposed an efficient parallel framework which includes dimensionality-reduction as well as data-size reduction techniques to improve k-means processing time and initial seeding problem. Our proposed parallel framework leverages the multi-node and multi-core architectures of a typical commodity cluster. We have validated our proposed approaches with real and synthetic datasets in parallel environment setup. The experimental results clearly show the significant improvements in k-means performance. |
URI: | https://dl.acm.org/doi/10.1145/2835043.2835060 http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8154 |
Appears in Collections: | Department of Computer Science and Information Systems |
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