Performance Analysis of Enhanced Clustering Algorithm for Gene Expression Data

TL;DR

This paper introduces an enhanced clustering algorithm, EAGMFI, for gene expression data that improves cluster quality and reduces the need for prior parameter specification, demonstrated through experimental validation.

Contribution

The paper proposes the EAGMFI algorithm, an improved version of AGMFI, which better determines the number of clusters and initializes centroids effectively for gene expression data.

Findings

EAGMFI outperforms AGMFI in identifying compact clusters.

The proposed method achieves higher Silhouette Coefficient scores.

Experimental results validate the effectiveness of EAGMFI in bioinformatics clustering tasks.

Abstract

Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. It is used to identify the co-expressed genes in specific cells or tissues that are actively used to make proteins. This method is used to analysis the gene expression, an important task in bioinformatics research. Cluster analysis of gene expression data has proved to be a useful tool for identifying co-expressed genes, biologically relevant groupings of genes and samples. In this paper we applied K-Means with Automatic Generations of Merge Factor for ISODATA- AGMFI. Though AGMFI has been applied for clustering of Gene Expression Data, this proposed Enhanced Automatic Generations of Merge Factor for ISODATA- EAGMFI Algorithms overcome the drawbacks of AGMFI in terms of specifying the optimal number of clusters and initialization of good cluster centroids. Experimental results on Gene Expression Data show that the proposed EAGMFI algorithms could identify compact clusters with perform well in terms of the Silhouette Coefficients cluster measure.