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A New Distance Measure for Model-Based Sequence Clustering

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3 Author(s)
Garcia-Garcia, D. ; Dept. of Signal Theor. & Commun., Univ. Carlos III of Madrid, Leganes ; Hernandez, E.P. ; Diaz de Maria, F.

We review the existing alternatives for defining model-based distances for clustering sequences and propose a new one based on the Kullback-Leibler divergence. This distance is shown to be especially useful in combination with spectral clustering. For improved performance in real-world scenarios, a model selection scheme is also proposed.

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:31 ,  Issue: 7 )