Title :
A New Distance Measure for Model-Based Sequence Clustering
Author :
Garcia-Garcia, Daniel ; Hernandez, E.P. ; Diaz de Maria, F.
Author_Institution :
Dept. of Signal Theor. & Commun., Univ. Carlos III of Madrid, Leganes
fDate :
7/1/2009 12:00:00 AM
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.
Keywords :
pattern clustering; Kullback-Leibler divergence; model selection scheme; model-based distances; model-based sequence clustering; Clustering; Similarity measures; sequence clustering; sequential data; similarity measures.; Algorithms; Artificial Intelligence; Cluster Analysis; Computer Simulation; Information Storage and Retrieval; Models, Statistical; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Sequence Analysis;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2008.268
