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Data clustering techniques are proven to be a successful data mining technique in the analysis of gene expression data. In the proposed work a novel clustering algorithm has been proposed which uses mixture of methodologies to overcome the drawbacks in the traditional clustering algorithms. The distinct characteristic of this algorithm is that it integrates the validation technique to improve the quality of clustering and principal component analysis to reduce the dimensionality of the data set to the clustering process. In addition the clustering algorithm incorporates computational intelligence technique to classify gene expression data efficiently. The empirical results proved that this new algorithm automatically produces the optimal clusters in a much faster way than the commonly used clustering methods. The resulting clusters are particularly attractive in numerous applications like gene behavior analysis, disease mapping and molecular biological processes to extract subject specific knowledge.