Clustering Approaches for Evaluation and Analysis on Formal Gene Expression Cancer Datasets

Abstract

Enormous generation of biological data and the need of analysis of that data led to the generation of the field Bioinformatics. Data mining is the stream which is used to derive, analyze the data by exploring the hidden patterns of the biological data. Though, data mining can be used in analyzing biological data such as genomic data, proteomic data here Gene Expression (GE) Data is considered for evaluation. GE is generated from Microarrays such as DNA and oligo micro arrays. The generated data is analyzed through the clustering techniques of data mining. This study deals with an implement the basic clustering approach K-Means and various clustering approaches like Hierarchal, Som, Click and basic fuzzy based clustering approach. Eventually, the comparative study of those approaches which lead to the effective approach of cluster analysis of GE.The experimental results shows that proposed algorithm achieve a higher clustering accuracy and takes less clustering time when compared with existing algorithms.