A New Distance Measure for Model-Based Sequence Clustering

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 31; no. 7; pp. 1325 - 1331
• Main Authors: Garcia-Garcia, D., Hernandez, E.P., Diaz de Maria, F.
• Format: Journal Article
• Language: English
• Published: Los Alamitos, CA IEEE 01.07.2009; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Artificial Intelligence; Character generation; Cluster Analysis; Clustering; Clustering algorithms; Clustering methods; Computational complexity; Computer science; control theory; systems; Computer Simulation; Data processing. List processing. Character string processing; Divergence; Exact sciences and technology; Hidden Markov models; Information Storage and Retrieval - methods; Intelligence; Learning systems; Machine learning; Machine learning algorithms; Memory organisation. Data processing; Models, Statistical; Pattern analysis; Pattern Recognition, Automated - methods; Performance enhancement; Proposals; Reproducibility of Results; Sensitivity and Specificity; Sequence Analysis - methods; sequential data; similarity measures; Software; Spectra; Similarity measures; sequence clustering; Cluster analysis; similarity measures; Divergence; sequential data; Similarity; Classification; Metric; Clustering
• ISSN: 0162-8828; 1939-3539
• DOI: 10.1109/TPAMI.2008.268

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.