  Spectral clustering is one of the most prominent clustering approaches. The
distance-based similarity is the most widely used method for spectral
clustering. However, people have already noticed that this is not suitable for
multi-scale data, as the distance varies a lot for clusters with different
densities. State of the art(ROSC and CAST ) addresses this limitation by taking
the reachability similarity of objects into account. However, we observe that
in real-world scenarios, data in the same cluster tend to present in a smooth
manner, and previous algorithms never take this into account. Based on this
observation, we propose a novel clustering algorithm, which con-siders the
smoothness of data for the first time. We first divide objects into a great
many tiny clusters. Our key idea is to cluster tiny clusters, whose centers
constitute smooth graphs. Theoretical analysis and experimental results show
that our clustering algorithm significantly outperforms state of the art.
Although in this paper, we singly focus on multi-scale situations, the idea of
data smoothness can certainly be extended to any clustering algorithms
