An Competent Artificial Bee Colony (ABC) and Fuzzy C Means Clustering Using Neuro-Fuzzy Discriminant Analysis from Gene Expression Data

Sathishkumar , Dr.V.Thiagarasu , Dr.E.Balamurugan , Dr.M.Ramalingam

Gene expression data, Bimax Algorithm, Neuro- fuzzy Discriminant Analysis, Artificial Bee Colony, Fuzzy C Means, Dimensionality Reduction.

This paper looks at the gene microarray data based on the pattern of gene expression using various clustering algorithms. To overcome the problems in gene expression analysis which are generally overlooked by the out-dated clustering algorithms we propose a novel algorithms for finding the co-regulated clusters, dimensionality reduction and clustering. The co-regulated clusters are determined using bi-correlation clustering algorithm (BCCA), also known as co-regulated biclusters. BCCA partakes erected bright to fruitage a numerous settled of biclusters of co-regulated genes over a slice of samples where all the genes in a bicluster have a similar alteration. The dimensionality diminution of microarray gene appearance data is carried out using Neuro - Fuzzy Discriminant Analysis (NFDA). To endure pledge amid the localities in area, NFDA is used and a well-organized Meta experiential optimization algorithm called Artificial Bee Colony (ABC) using Fuzzy C Means clustering is used for clustering the gene expression built on the strategy. The investigational results show that the proposed algorithm achieved a higher clustering accurateness and takes less clustering time when associated with existing algorithms.

Volume 3 Issue 4, April-2018

Pages : 24 - 28