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Please use this identifier to cite or link to this item:
http://hdl.handle.net/10016/8978
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| Title: | A new distance measure for model-based sequence clustering |
| Author(s): | García-García, Darío
Parrado Hernández, Emilio
Díaz-de-María, Fernando |
| Publisher: | IEEE |
| Issued date: | Jul-2009 |
| Citation: | IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, Nº 7, pp. 1325-1331, Oct. 2009 |
| URI: | http://hdl.handle.net/10016/8978 |
| ISSN: | 0162-8828 |
| DOI: | 10.1109/TPAMI.2008.268 |
| 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. |
| Review: | PeerReviewed |
| Publisher version: | http://dx.doi.org/10.1109/TPAMI.2008.268 |
| Keywords: | Clustering
Similarity measures
Sequence clustering |
| Rights: | © IEEE |
| Appears in Collections: | DTSC - GPM - Artículos de Revistas
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