Unsupervised Gene Expression Data using Enhanced Clustering Method

TL;DR

This paper introduces an unsupervised gene expression clustering method using an enhanced initialization algorithm with K-Means, improving cluster compactness and reducing the need for pre-specifying cluster numbers.

Contribution

It proposes the ECIA algorithm to improve K-Means clustering for gene expression data, addressing centroid initialization and optimal cluster number determination.

Findings

Achieves higher Silhouette Coefficient scores indicating better clustering quality.

Identifies compact and meaningful gene clusters.

Reduces the need for prior knowledge of the number of clusters.

Abstract

Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. Identification of co-expressed genes and coherent patterns is the central goal in microarray or gene expression data analysis and is an important task in bioinformatics research. Feature selection is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. In this work the unsupervised Gene selection method and Enhanced Center Initialization Algorithm (ECIA) with K-Means algorithms have been applied for clustering of Gene Expression Data. This proposed clustering algorithm overcomes the drawbacks in terms of specifying the optimal number of clusters and initialization of good cluster centroids. Gene Expression Data show that could identify compact clusters with performs well in terms of the Silhouette Coefficients cluster measure.