title: A new distance measure for model-based sequence clustering.
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.
authors.affiliation: Department of Signal Theory and Communications, University Carlos III of Madrid, Leganés, Madrid, Spain. dggarcia@tsc.uc3m.es
authors.forename: Darío
authors.initials: D
authors.lastname: García-García
authors.forename: Emilio
authors.initials: E
authors.lastname: Parrado Hernández
authors.forename: Fernando
authors.initials: F
authors.lastname: Díaz-de María
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017421:Sequence Analysis / Q000379:methods*