Computer Science – Computational Engineering – Finance – and Science
Scientific paper
2009-09-08
International Journal of Computer Science and Information Security, IJCSIS, Vol. 4, No. 1 & 2, August 2009, USA
Computer Science
Computational Engineering, Finance, and Science
6 Pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS 2009, ISSN 1947 5500, Impact F
Scientific paper
In recent years, with the development of microarray technique, discovery of useful knowledge from microarray data has become very important. Biclustering is a very useful data mining technique for discovering genes which have similar behavior. In microarray data, several objectives have to be optimized simultaneously and often these objectives are in conflict with each other. A Multi Objective model is capable of solving such problems. Our method proposes a Hybrid algorithm which is based on the Multi Objective Particle Swarm Optimization for discovering biclusters in gene expression data. In our method, we will consider a low level of overlapping amongst the biclusters and try to cover all elements of the gene expression matrix. Experimental results in the bench mark database show a significant improvement in both overlap among biclusters and coverage of elements in the gene expression matrix.
Dastjerdi Ahmad Baraani
lashkargir Mohsen
Monadjemi Amirhassan S.
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