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Abstract and Applied Analysis
Volume 2013, Article ID 183410, 7 pages
Research Article

A Dynamic Fuzzy Cluster Algorithm for Time Series

1School of Computer Science, Liaoning Normal University, Dalian, Liaoning 116081, China
2School of Urban and Environmental Science, Liaoning Normal University, Dalian, Liaoning 116029, China
3The School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China

Received 19 December 2012; Accepted 25 March 2013

Academic Editor: Jianhong (Cecilia) Xia

Copyright © 2013 Min Ji et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 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.