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A dynamic fuzzy cluster algorithm for time series. (English) Zbl 1272.68391

Summary: This paper presents an efficient algorithm, called dynamic fuzzy cluster (DFC), for dynamically clustering time series by introducing the definition of key point and improving the FCM algorithm. The proposed algorithm works by determining those time series whose class labels are vague and further partitions them into different clusters over time. The main advantage of this approach compared with other existing algorithms is that the property of some time series belonging to different clusters over time can be partially revealed. Results from simulation-based experiments on geographical data demonstrate the excellent performance and the desired results have been obtained. The proposed algorithm can be applied to solve other clustering problems in data mining.

MSC:

68T37 Reasoning under uncertainty in the context of artificial intelligence
68T10 Pattern recognition, speech recognition
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)

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