Microarray gene expression data technology has enhanced the Genome research to a great extent by making it possible in areas like genetic profiling, gene clustering, gene mapping and gene annotation. Gene clustering algorithms are used to cluster genes which have similar expressions or coexpressed genes in the same cluster by using distance based proximity measures. In clustering, the genes in the same cluster tend to be highly similar and decrease its similarity to those genes in different clusters. The quality computation or the validation measurement of the clusters is the challenge in today's genome wide research. In this paper we have implemented various standard clustering algorithms like k-means, hierarchical, SOM by incorporating the cluster validity techniques like Hubert's Statistics, FOM, Fowlkes-Mallow index, Jaccard Index, Rand Index and F-Measure techniques. We have used real dataset of publicly available microarray gene data. We conducted an exploratory statistical analysis on the results of the clustering algorithms to determine its validity of the clusters formed. We also performed a comparative study between these clustering algorithms used in our study in terms of the indices values. © Research India Publications.