18th International Conference on Pattern Recognition (ICPR'06) Volume 3 An Interweaved HMM/DTW Approach to Robust Time Series Clustering Hong Kong August 20-August 24 ISBN: 0-7695-2521-0
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.257
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.
Citation:
Jianying Hu, Bonnie Ray, Lanshan Han, "An Interweaved HMM/DTW Approach to Robust Time Series Clustering," icpr, vol. 3, pp.145-148, 18th International Conference on Pattern Recognition (ICPR'06) Volume 3, 2006 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||