Mining Gene Expression Data Using PCA Based Clustering

As the amount of laboratory data in molecular biology and bioinformatics grows exponentially in each year due to advanced technologies such as DNA Microarray, new efficient and effective clustering methods must be developed to process this fast growing amount of biological data. Numerous clustering techniques have been applied in the analysis of gene expression data to extract biologically significant patterns. But there are issues like clustering quality, high dimensionality of input data and computational efficiency need to be addressed. A novel hybrid clustering algorithm is proposed, which is a blend of Principal Component Analysis (PCA) and the enhanced correlation based clustering. PCA is a classical statistic technique for finding patterns in data of high dimension. The empirical results show that this approach provides more stable clustering performance in terms of quality and efficiency. The resulting clusters offer potential insight into gene function, molecular biological processes and regulatory mechanisms.