Publication:
A new distance measure for model-based sequence clustering

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URI: https://hdl.handle.net/10016/8978
ISSN: 0162-8828
DOI: 10.1109/TPAMI.2008.268
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2009-07
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IEEE
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Abstract
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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Clustering, Similarity measures, Sequence clustering
Bibliographic citation
IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, Nº 7, pp. 1325-1331, Oct. 2009
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DTSC - GPM - Artículos de Revistas