tailieunhanh - Some Improvements of Fuzzy Clustering Algorithms Using Picture Fuzzy Sets and Applications for Geographic Data Clustering
This paper summarizes the major findings of the research project under the code name . The research aims to enhancement of some fuzzy clustering methods by the mean of more generalized fuzzy sets. The main results are: Improve a distributed fuzzy clustering method for big data using picture fuzzy sets; design a novel method called DPFCM to reduce communication cost using the facilitator model (instead of the peer-to-peer model) and the picture fuzzy sets. | VNU Journal of Science: Comp. Science & Com. Eng., Vol. 32, No. 3 (2016) 32-38 Some Improvements of Fuzzy Clustering Algorithms Using Picture Fuzzy Sets and Applications for Geographic Data Clustering Nguyen Dinh Hoa1,*, Le Hoang Son2 , Pham Huy Thong2 1 VNU Information Technology Institute, 144 Xuan Thuy, Cau Giay, Hanoi, Vietnam 2 VNU University of Science, 334 Nguyen Trai, Thanh Xuan, Hanoi, Vietnam Abstract This paper summarizes the major findings of the research project under the code name . The research aims to enhancement of some fuzzy clustering methods by the mean of more generalized fuzzy sets. The main results are: (1) Improve a distributed fuzzy clustering method for big data using picture fuzzy sets; design a novel method called DPFCM to reduce communication cost using the facilitator model (instead of the peer-to-peer model) and the picture fuzzy sets. The experimental evaluations show that the clustering quality of DPFCM is better than the original algorithm while ensuring reasonable computational time. (2) Apply picture fuzzy clustering for weather nowcasting problems in a novel method called PFS-STAR that integrates the STAR technique and picture fuzzy clustering to enhance the forecast accuracy. Experimental results on the satellite image sequences show that the proposed method is better than the related works, especially in rain predicting. (3) Develop a GIS plug-in software that implemented some improved fuzzy clustering algorithms. The tool supports access to spatial databases and visualization of clustering results in thematic map layers. Received 20 June 2016, Revised 04 October 2016, Accepted 18 October 2016 Keywords: Spatial clustering, fuzzy clustering, distributed clustering, picture fuzzy set, weather nowcasting, spatio-temporal regression. 1. Introduction* (GIS) has many challenges. The database of GIS contains large amounts of data, which increases day by day; the data volume to be processed is often large, even very large
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