document: A hybrid unsupervised approach for document clustering We propose a hybrid , unsupervised document clustering approach that combines a hierarchical clustering algorithm with Expectation Maximization . We developed several heuristics to automatically select a subset of the clusters generated by the first algorithm as the initial points of the second one . Furthermore , our initialization algorithm generates not only an initial model for the iterative refinement algorithm but also an estimate of the model dimension , thus eliminating another important element of human supervision . We have evaluated the proposed system on five real-world document collections . The results show that our approach generates clustering solutions of higher quality than both its individual components .
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