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Issue No. 07 - July (2009 vol. 31)
ISSN: 0162-8828
pp: 1325-1331
Darío García-García , University Carlos III of Madrid, Madrid
Emilio Parrado Hernández , University Carlos III of Madrid, Madrid
Fernando Díaz-de María , University Carlos III of Madrid, Madrid
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.
Clustering, sequential data, similarity measures.

D. García-García, F. Díaz-de María and E. Parrado Hernández, "A New Distance Measure for Model-Based Sequence Clustering," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 31, no. , pp. 1325-1331, 2008.
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