Multi-Level Clustering Algorithm for Star/Galaxy Separation

With the coming era of astronomic data with mass, high dimensionality and nonlinearity, clustering astronomic data becomes more and more important. This paper proposed a new clustering algorithm, which reduces the space and time complexity and the sensitivity to the parameters. It is suitable for processing large scale astronomic data sets. The new algorithm consists of three phases: coarsening clustering, representative data clustering and merging. First, we use Affinity Propagation (AP) algorithm for coarsening. Specifically, in order to save the space and computational cost, we only compute the similarity between each point and its t nearest neighbors, and get a coarsened similarity matrix (with only t columns, where t