Abstract
Data Clustering is a descriptive data mining task of finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups [5]. The motivation behind this research paper is to explore KMeans partitioning algorithm in the currently available parallel architecture using parallel programming models. Parallel KMeans algorithms have been implemented for a shared memory model using OpenMP programming and distributed memory model using MPI programming. A hybrid version of OpenMP in MPI programming also has been experimented. The performance of the parallel algorithms were analysed to compare the speedup obtained and to study the Amdhals effect. The computational time of hybrid method was reduced by 50% compared to MPI and was also more efficient with balanced load.
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Mohanavalli, S., Jaisakthi, S.M., Aravindan, C. (2011). Strategies for Parallelizing KMeans Data Clustering Algorithm. In: Das, V.V., Thomas, G., Lumban Gaol, F. (eds) Information Technology and Mobile Communication. AIM 2011. Communications in Computer and Information Science, vol 147. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20573-6_76
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DOI: https://doi.org/10.1007/978-3-642-20573-6_76
Publisher Name: Springer, Berlin, Heidelberg
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