An information granulation-based fuzzy clustering method for time series segmentation

Time series segmentation aims to extract some meaningful subsequences from complex temporal information. A proper segmentation can effectively help users to analyze the structure of time series. In this study, we propose an information granulation-based fuzzy clustering method for the problem of time series segmentation. The suggested time series segmentation method follows the technological procedure of fuzzy c-means clustering method. First, the original time series is randomly divided into several segments. Then, an information granulation-based dynamic time warping approach is designed to update the series centers, where the principle of reasonable granularity is utilized to calculate the mean of the segments. Next, the time series segments are clustered by optimizing the objective function. Finally, the optimal segmentation points are generated by merging the contiguous segments in the same cluster. The experimental results show that the established segmentation method has more advantages than the existing segmentation methods.