It is possible to simultaneously monitor the expression levels of huge number of genes during important biological processes and across collections of related samples by DNA microarray technology. But, the huge number of genes and the complicacy of biological networks increases the challenges of interpreting the results which includes large number of measurements. In this paper, we have analysed and reduced the high dimensional data by applying various clustering techniques to identify interesting and useful patterns. Then, heatmap is used for the visualisation of clustered and biclustered data. All the analysis is done using R software. The analysis educates us about correlation of various genes by identifying patterns which will surely help us to better understand the structure and function of human genes and thereby develop new strategies to take action to reduce human diseases.