A novel clustering method on time series data
Expert Systems with Applications: An International Journal
Weighted dynamic time warping for time series classification
Pattern Recognition
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Dynamic Time Warping is proposed. This method is based on the granular approach of information granulation and has the characteristics of Dynamic Time Warping approach. Thus it can be used to cluster time series with different lengths on the granular level. To cluster time series, this method first builds the corresponding granular time series, and then does the clustering on the granular time series. With this method, higher efficiency will be achieved in clustering time series, which is a goal pursued in clustering of large amount of time series. We also illustrate the prior performance of the new method with experiments.