Spectral clustering algorithm based on adaptive neighbor distance sort order

Spectral clustering has been widely used in data mining in the past years. The performance of spectral clustering is very sensitive to the selection of scale parameter. Especially, when data has multi-scale it is very difficult to find a proper value for the scale parameter. To solve the problem, an improved method based on adaptive neighbor distance sort order has been proposed in this paper. The method enlarges the affinity between two points in the same cluster and reduces that in different clusters. Our experiments on the synthetic and real life datasets have shown promising results comparing with tradition method and k-means.