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Clustering analysis has been an emerging research issue in data mining due its variety of applications. In the recent years, it has become an essential tool for gene expression analysis. Many clustering algorithms have been proposed so far. However, each algorithm has its own merits and demerits and can not work for all real situations. In this paper, we present a clustering algorithm that is inspired by minimum spanning tree. To automate and evaluate our algorithm, we incorporate the concept of ratio between the intra-cluster distance (measuring compactness) and the inter-cluster distance (measuring isolation). Experimental results on some complex as well as real world data sets reveal that the proposed algorithm is efficient and competitive with the existing clustering algorithms.