The week's pick
Reseach Article
Performance Improvement through Parallelization of Graph Clustering algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 93 - Number 9 |
| Year of Publication: 2014 |
| Authors: Yogendra Kumar Dehariya, Ravi Shankar Singh |
10.5120/16241-5791
|
Yogendra Kumar Dehariya, Ravi Shankar Singh . Performance Improvement through Parallelization of Graph Clustering algorithm. International Journal of Computer Applications. 93, 9 ( May 2014), 7-10. DOI=10.5120/16241-5791
Abstract
Clustering is the task of Grouping of elements or nodes (in the case of graph) in to clusters or subgroup based on some similarity metrics. In general Clustering is unsupervised learning task requires very little or prior knowledge except the data set. However Clustering Task are computationally expensive as most of the algorithms require recursion or iterations and most of the time we have to deal with real life data set which are generally very huge in size. This paper deals with a well-known clustering algorithm MCL (Markov Clustering Algorithm) and proposes a parallel version of it using OPENMP.
References
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- Yogendra Kumar Dehariya,"Comparative Analysis of graph Clustering algorithm using Bloggers Data",ICICT 2014.
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