Scalability of Parallel Genetic Algorithm for Two-mode Clustering
Briti Deb, Satish Narayana Srirama · International Journal of Computer Applications · 2014
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 twomode 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.