A new distance measure for model-based sequence clustering

IEEE Trans Pattern Anal Mach Intell. 2009 Jul;31(7):1325-31. 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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Artificial Intelligence*
  • Cluster Analysis
  • Computer Simulation
  • Information Storage and Retrieval / methods*
  • Models, Statistical
  • Pattern Recognition, Automated / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Sequence Analysis / methods*