DocumentCode
980460
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
Volume
31
Issue
7
fYear
2009
fDate
7/1/2009 12:00:00 AM
Firstpage
1325
Lastpage
1331
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;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/TPAMI.2008.268
Filename
4668349
Link To Document