Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TPAMI.2008.268 | IEEE Trans. Pattern Anal. Mach. Intell. |
Keywords | Field | DocType |
model selection scheme,clustering sequence,model-based sequence clustering,existing alternative,new distance measure,improved performance,spectral clustering,kullback-leibler divergence,real-world scenario,model-based distance,kullback leibler divergence,model selection,machine learning,clustering,cluster analysis,computer simulation,hidden markov models,computational complexity,sequence analysis,algorithms,artificial intelligence,clustering algorithms | k-medians clustering,Hierarchical clustering,Data mining,Fuzzy clustering,CURE data clustering algorithm,Pattern recognition,Correlation clustering,Computer science,Constrained clustering,Artificial intelligence,Cluster analysis,Single-linkage clustering | Journal |
Volume | Issue | ISSN |
31 | 7 | 0162-8828 |
Citations | PageRank | References |
14 | 1.11 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Darío García-García | 1 | 24 | 4.41 |
| Emilio Parrado Hernández | 2 | 14 | 1.45 |
| Fernando Díaz-de-María | 3 | 201 | 32.14 |
| Diaz de Maria, F. | 4 | 14 | 1.11 |