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Clustering, known as divide a set of static data points into densely distributed groups, has long been a well-known research area. However, many real-life problems require a novel and generalized form of clustering, the evolutionary clustering. Given a dynamic set of data points that may move, disappear and emerge, the evolutionary clustering is to track the move, disappear and emerge of the corresponding clusters. In this paper, we propose converting this novel problem into an iterative form of learning a mixture model, and present a structural-EM algorithm as the solution.