Some Improvements of Fuzzy Clustering Algorithms Using Picture Fuzzy Sets and Applications For Geographic Data Clustering

Abstract

This paper summarizes the major findings of the research project under the code name QG.14.60. 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 problem 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.