document: Evolutionary clustering We consider the problem of clustering data over time . An evolutionary clustering should simultaneously optimize two potentially conflicting criteria : first , the clustering at any point in time should remain faithful to the current data as much as possible ; and second , the clustering should not shift dramatically from one timestep to the next . We present a generic framework for this problem , and discuss evolutionary versions of two widely-used clustering algorithms within this framework : k-means and agglomerative hierarchical clustering . We extensively evaluate these algorithms on real data sets and show that our algorithms can simultaneously attain both high accuracy in capturing today 's data , and high fidelity in reflecting yesterday 's clustering .
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