Scale parameter selection for the spectral clustering method

Spectral clustering is a powerful method for finding structure in data through the eigenvectors of a similarity matrix. It often out-performs traditional clustering algorithms such as k-means when the structure of the individual clusters is highly non-convex. Its accuracy depends on how the similarity between pairs of data points is defined. When a Gaussian similarity function is used, the choice of a scale parameter o is crucial. It is often suggested to select o by running the spectral algorithm repeatedly for different values of o and selecting the one that provides the best clustering according to some criterium. In this paper we propose a low cost technique for selecting a suitable o based on the minimal spanning tree (MST) associated to the graph of the distances between pairs of points. A numerical experimentation on both artificial and real-world datasets validates the effectiveness of the proposed technique