1 research outputs found
A Hybrid Multi Objective Particle Swarm Optimization Method to Discover Biclusters in Microarray Data
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.Comment: 6 Pages IEEE format, International Journal of Computer Science and
Information Security, IJCSIS 2009, ISSN 1947 5500, Impact Factor 0.423,
http://sites.google.com/site/ijcsis