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Hybrid methods for feature selection comprised of combination of filter and wrapper approaches have recently been emerged as strong techniques for the problem in this domain. In this work we have proposed three simple hybrid approaches for reducing data dimensionality while maintaining classification accuracy which combine our basic feature selection through feature clustering (FSFC) approach to other standard approaches of feature selection in different orientation. We have employed popular ROC curve analysis to evaluate experimental outcome. Our experimental results clearly show suitability of our methods in hybrid approaches of feature selection in micro-array gene expression domain.