The problem of clustering has been an important problem since the early 20th century and several possible solutions were proposed. With the rise of computing machines clustering has become an important part of many data mining tasks, focussed on fast implementations. An important task related to clustering is image segmentation. In the set of solutions to the clustering problem, the method of spectral clustering has obtained wide interest due to its ability to detect non-convex clusters in the data. In this article, we propose a fast alternative to the spectral clustering, obtained by taking the Γ−limit. We explore the links between the new method and MST based clustering. We then show that the proposed method is as good as the spectral clustering with the help of experiments on several datasets. We also show that the new method is scalable to large data unlike the classical spectral clustering methods.