International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 94 - Issue 14 |
Published: May 2014 |
Authors: Briti Deb, Satish Narayana Srirama |
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Briti Deb, Satish Narayana Srirama . Scalability of Parallel Genetic Algorithm for Two-mode Clustering. International Journal of Computer Applications. 94, 14 (May 2014), 23-26. DOI=10.5120/16411-5829
@article{ 10.5120/16411-5829, author = { Briti Deb,Satish Narayana Srirama }, title = { Scalability of Parallel Genetic Algorithm for Two-mode Clustering }, journal = { International Journal of Computer Applications }, year = { 2014 }, volume = { 94 }, number = { 14 }, pages = { 23-26 }, doi = { 10.5120/16411-5829 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2014 %A Briti Deb %A Satish Narayana Srirama %T Scalability of Parallel Genetic Algorithm for Two-mode Clustering%T %J International Journal of Computer Applications %V 94 %N 14 %P 23-26 %R 10.5120/16411-5829 %I Foundation of Computer Science (FCS), NY, USA
Data matrix having the same set of entity in the rows and cloumns is known as one-mode data matrix, and traditional one-mode clustering algorithms can be used to cluster the rows (or columns) separately. With the popularity of use of two-mode data matrices where the rows and columns have different sets of entities, the need for simultaneous clustering of rows and columns popularly known as two-mode clustering increased. Additionally, the emergence of large data sets and the prediction of Moore's law slow-down have created the challenge of clustering scalability. In this paper, we address the problem of scalability of organizing an unlabelled two-mode dataset into clusters utilizing multicore processor. We propose a parallel genetic algorithm (GA) heuristics based two-mode clustering algorithm, which is an adaptation of the classical Cuthill-McKee Matrix Bandwidth Minimization (MBM) algorithm. The classical MBM method aims at reducing the bandwidth of a sparse symmetric matrix, which we adapted to make it suitable for non-symmetric real-valued matrix. Preliminary results indicate that our algorithm is scalable on multicore processor compared to serial implementation. Future work will include more extensive experiments and evaluations of the system.