Name
Abstract | ||
|---|---|---|
We introduce an approach for model-based sequence clustering that addresses several drawbacks of existing algorithms. The approach uses a combination of Hidden Markov Models (HMMs) for sequence estimation and Dynamic Time Warping (DTW) for hierarchical clustering, with interlocking steps of model selection, estimation and sequence grouping. We demonstrate experimentally that the algorithm can effectively handle sequences of widely varying lengths, unbalanced cluster sizes, as well as outliers. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICPR.2006.257 | ICPR (3) |
Keywords | Field | DocType |
hidden markov models,dynamic time warping,dtw approach,interweaved hmm,model-based sequence clustering,sequence estimation,hierarchical clustering,robust time series clustering,varying length,interlocking step,unbalanced cluster size,model selection,sequence grouping,hidden markov model,time series | Sequence clustering,Hierarchical clustering,CURE data clustering algorithm,Pattern recognition,Dynamic time warping,Computer science,Outlier,Model selection,Artificial intelligence,Cluster analysis,Hidden Markov model | Conference |
ISSN | ISBN | Citations |
1051-4651 | 0-7695-2521-0 | 10 |
PageRank | References | Authors |
0.82 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianying Hu | 1 | 478 | 35.52 |
| Bonnie Ray | 2 | 46 | 4.17 |
| Lanshan Han | 3 | 50 | 4.66 |