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Spectral clustering has gained importance in recent years due to its abilityto cluster complex data as it requires only pairwise similarity among datapoints with its ease of implementation. The central point in spectralclustering is the process of capturing pair-wise similarity. In the literature,many research techniques have been proposed for effective construction ofaffinity matrix with suitable pair- wise similarity. In this paper a generalframework for capturing pairwise affinity using local features such as density,proximity and structural similarity is been proposed. Topological Node Featuresare exploited to define the notion of density and local structure. These localfeatures are incorporated into the construction of the affinity matrix.Experimental results, on widely used datasets such as synthetic shape datasets,UCI real datasets and MNIST handwritten datasets show that the proposedframework outperforms standard spectral clustering methods.

Novel Framework for Spectral Clustering using Topological Node Features(TNF)