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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Goyal, Navneet | - |
dc.contributor.author | Goyal, Poonam | - |
dc.date.accessioned | 2022-12-27T07:08:38Z | - |
dc.date.available | 2022-12-27T07:08:38Z | - |
dc.date.issued | 2015-10 | - |
dc.identifier.uri | https://dl.acm.org/doi/10.1145/2835043.2835060 | - |
dc.identifier.uri | http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8154 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | ACM Digital Library | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Clustering | en_US |
dc.subject | Clustering framework | en_US |
dc.subject | Parallel framework | en_US |
dc.title | Parallel Framework for Efficient k-means Clustering | en_US |
dc.type | Article | en_US |
Appears in Collections: | Department of Computer Science and Information Systems |
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