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Scalability of Parallel Genetic Algorithm for Two-mode Clustering
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 94 - Number 14 |
| Year of Publication: 2014 |
| Authors: Briti Deb, Satish Narayana Srirama |
10.5120/16411-5829
|
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
Abstract
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.
References
- Suderman, M. , & Hallett, M. 2007. Tools for visually exploring biological networks. Bioinformatics, 23(20), 2651-2659.
- Chu, Cheng-Tao, et al. Map-reduce for machine learning on multicore. NIPS. Vol. 6. (2006).
- Borgatti, Stephen P. , and Martin G. Everett. Network analysis of 2-mode data. Social networks 19. 3 (1997): 243-269.
- Van Mechelen, Iven, Hans-Hermann Bock, and Paul De Boeck. Two-mode clustering methods: a structured overview. Statistical methods in medical research 13. 5 (2004): 363-394.
- E. Cuthill and J. McKee. Reducing the bandwidth of sparse symmetric matrices In Proc. 24th Nat. Conf. ACM, pages 157–172, (1969).
- Hageman, J. A. , van den Berg, R. A. , Westerhuis, J. A. , van der Werf, M. J. , & Smilde, A. K. (2008). Genetic algorithm based two-mode clustering of metabolomics data. Metabolomics, 4(2), 141-149.
- Estivill-Castro, Vladimir (June 2002). Why so many clustering algorithms — A Position Paper. ACM SIGKDD Explorations Newsletter 4 (1): 65–75.
- Cheng, Y. , & Church, G. M. (2000, August). Biclustering of expression data. In ISMB, Vol. 8, pp. 93-103.
- Dhillon, I. S. 2001, August. Co-clustering documents and words using bipartite spectral graph partitioning. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 269-274. ACM.
- Liiv, I. 2010. Seriation and matrix reordering methods: An historical overview. Statistical analysis and data mining, 3(2), 70-91.
- Schaeffer, S. E. 2007. Graph clustering. Computer Science Review, 1(1), 27-64.
- Esposito, A. , Catalano, M. S. , Malucelli, F. , & Tarricone, L. 1998. A new matrix bandwidth reduction algorithm. Operations Research Letters, 23(3), 99-107.
- Hageman, J. A. , Malosetti, M. , & Van Eeuwijk, F. A. 2012. Two-mode clustering of genotype by trait and genotype by environment data. Euphytica, 183(3), 349-359.
- Zhang, P. , Wang, J. , Li, X. , Li, M. , Di, Z. , & Fan, Y. 2008. Clustering coefficient and community structure of bipartite networks. Physica A: Statistical Mechanics and its Applications, 387(27), 6869-6875.
- Arabie, P. , Schleutermann, S. , Daws, J. , and Hubert, L. 1988, Marketing Applications of Sequencing and Partitioning of Nonsymmetric and/or Two-Mode Matrices, in Data, Expert Knowledge and Decisions, Eds. W. Gaul, and M. Schader, Berlin: Springer-Verlag, 215-224.
- Fonseca, Y. C. F. A bipartite graph co-clustering approach to ontology mapping, Proceedings of the Workshop on Semantic Web Technologies for Searching and Retrieving Scientific Data. Colocated with the Second International Semantic Web Conference (ISWC-03), CEUR-WS. org, 2003.
- Madeira, S. C. , & Oliveira, A. L. 2004. Biclustering algorithms for biological data analysis: a survey. Computational Biology and Bioinformatics, IEEE/ACM Transactions on, 1(1), 24-45. Chicago
- Scrucca, L. 2012. GA: a package for genetic algorithms in R. Journal of Statistical Software, Volume 53, Issue 4.
- Yeast expression matrix, URL accessed on 02-01-2014: http://arep. med. harvard. edu/biclustering/yeast. matrix
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