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A methodology used to discover crosscutting concerns in existing systems is called Aspect mining. The aim of this paper is to presents a common case study evaluating the existing aspect mining techniques proposed by authors itself. We apply two independently proposed aspect mining techniques K-means clustering and genetic algorithm on de-facto benchmark JHotDraw and evaluate and compare their results. The purpose of this paper is not to find the best technique between these two but ro discuss the strength and weakness of each in terms of every identified concern.