BI-CLUSTERING ALGORITHM FOR MICROARRAY GENE DATA BASED ON THE COMBINATION OF FCM AND LION OPTIMIZATION ALGORITHM
P. Edwin Dhas Dr. B. Sankara Gomathi
The exploitation of microarray gene data is increasing over time, owing to the technological advancement of biomedical science. The gene data reveals many important details and is necessary to analyse the data with intense care. The gene data analysis is one of the significant research areas and this work proposes an unsupervised way of gene analysis. The gene analysis can be carried out by supervised, semi-supervised or unsupervised techniques. Both the supervised and semi-supervised analysis requires the process of system training and it requires prior knowledge about the dataset. On the contrary, the unsupervised technique involves no training and the related gene data are grouped together without any prior arrangements. Hence, this work proposes an unsupervised bi-clustering algorithm for microarray gene data by combining the Fuzzy C Means (FCM) and Lion Optimization Algorithm (LOA). The performance of the proposed work is tested in terms of precision, recall, F-measure, rand index and time consumption. The experimental results prove the efficacy of the proposed clustering algorithm.
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