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The proliferation of microarray gene expression data have been analyzed by many popular data mining techniques. In that, clustering is one of the powerful tools in data mining which clusters the unlabeled sample into different clusters. The significant problem associated with almost in all un-supervised classification technique is specifying the number of clusters a priori. The results of these methods need to be evaluated so as to find the most suitable clustering technique for the given dataset because different clustering techniques produce different results. This issue can be solved by comparing the results of dataset applied on different clustering techniques and evaluate the performance of clustering techniques with clustering validity techniques.
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