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Clustering is the unsupervised classification of patterns into groups. A clustering algorithm partitions a data set into several groups such that similarity within a group is larger than among groups The clustering problem has been addressed in many contexts and by researchers in many disciplines, this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. There is need to develop some methods to build the software fault prediction model based on unsupervised learning which can help to predict the fault -- proneness of a program modules when fault labels for modules are not present. One of the such method is use of clustering techniques. This paper presents a case study of different clustering techniques and analyzes their performance.