Similarity dynamical clustering algorithm based on multidimensional shape features for time series
2017
WANG Ling | MENG Jian-yao | XU Pei-pei | PENG Kai-xiang
Traditional data mining methods are difficult to deal with the high dimensionality and dynamics characteristic of the time series. Therefore, in this study, a similarity dynamical clustering algorithm based on multidimensional shape features for time series (SDCTS) was proposed. First, the feature points of multidimensional time series are extracted to realize dimensionality reduction. Second, a new similarity measure criterion is defined with the shape features (slope, length, and amplitude) of the obtained multidimensional time series, and thus a dynamical clustering algorithm of multidimensional time series is proposed without predefining clustering numbers. The experimental results demonstrate that the SDCTS algorithm improves the clustering accuracy for time series compared with other algorithms.
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