Comparative Study of Data Cluster Analysis for Microarray

Microarray has been a popular method for representing biological data. Microarray technology allows biologists to monitor genome-wide patterns of gene expression in a high-throughput fashion. Clustering the biological sequences according to their components may reveal the biological functionality among the sequences. Data cluster analysis is an important task in microarray data. There is no clustering algorithm that can be universally used to solve all problems. Therefore in this paper comparative study of data cluster analysis for microarray is presented. Here the most popular cluster algorithms that can be applied for microarray data are discussed. The uncertainty of data, optimization and density estimation are considered for comparison.