A Dynamic Fuzzy Cluster Algorithm for Time Series | Zendy

Min Ji | Zendy; Fuding Xie | Zendy; Yu Ping | Zendy

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A Dynamic Fuzzy Cluster Algorithm for Time Series

Author(s) -

Min Ji,

Fuding Xie,

Yu Ping

Publication year - 2013

Publication title -

abstract and applied analysis

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.228

H-Index - 56

eISSN - 1687-0409

pISSN - 1085-3375

DOI - 10.1155/2013/183410

Subject(s) - series (stratigraphy) , cluster analysis , cluster (spacecraft) , fuzzy logic , key (lock) , point (geometry) , fuzzy clustering , algorithm , mathematics , data mining , time series , class (philosophy) , property (philosophy) , computer science , artificial intelligence , machine learning , paleontology , philosophy , geometry , computer security , epistemology , biology , programming language

This paper presents an efficient algorithm, called dynamic fuzzy cluster (DFC), for dynamicallyclustering 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

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