fuzzy cluster algorithm
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2020 ◽  
Vol 515 ◽  
pp. 280-293 ◽  
Author(s):  
Guangxia Xu ◽  
Linghao Zhang ◽  
Chuang Ma ◽  
Yanbing Liu

2013 ◽  
Vol 830 ◽  
pp. 341-344
Author(s):  
Jun Jun Du ◽  
Sheng Ping Jin ◽  
Qiong Li ◽  
She Sheng Zhang

Consider heavy metal pollution of topsoil in the city of world today is a hot science research project. A fuzzy clustering algorithm l is constructed ed by analyzing the propagation characteristics of heavy metal pollutants. Considering topography, areas, factories, roads, , irredentist, etc. we calculate a evaluation on comprehensive pollution, and the degree of heavy metals pollution, by using fuzzy clustering and fuzzy AHP. The results show that the index of the comprehensive pollution of heavy metals on the region, and the weight of pollution of each category.


2013 ◽  
Vol 110 (3) ◽  
pp. 447-454 ◽  
Author(s):  
Helton Hugo de Carvalho ◽  
Robson Luiz Moreno ◽  
Tales Cleber Pimenta ◽  
Paulo C. Crepaldi ◽  
Evaldo Cintra

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Min Ji ◽  
Fuding Xie ◽  
Yu Ping

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.


2012 ◽  
Vol 562-564 ◽  
pp. 855-860 ◽  
Author(s):  
Si Kun You ◽  
Xiao Chu Liu ◽  
Hong Guang Deng ◽  
Jun Liu

In this work, a novel method for sculptured surface subdivision to improve the machinery’s ability and efficiency in 5-axis CNC machining complex surface is introduced. The method subdivides automatically a monolithic convex or concave or simultaneously complex sculptured surface into a number of surface patches and achieves the goal of similar normal directions and small difference between the curvatures in every patch by using weight fuzzy cluster algorithm which takes the curvatures and normal vectors of the sculptured surface into account simultaneously. The inclination angle variation between every two Cutter Contact Points (CC Points) is decreased in every patch to avoid large-angle rotation of tool to save machining time when a flat-end mill is used. This work contributes to automated 5-axis CNC tool path generation for sculptured part machining and forms a foundation for further research.


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