A New Distance Measure for Model-Based Sequence Clustering

Authors:
Darío García-García;Emilio Parrado Hernández;Fernando Díaz-de María
Affiliations:
University Carlos III of Madrid, Madrid;University Carlos III of Madrid, Madrid;University Carlos III of Madrid, Madrid
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2009

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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.