A heart disease recognition embedded system with fuzzy cluster algorithm

2013 ◽  
Vol 110 (3) ◽  
pp. 447-454 ◽  
Author(s):  
Helton Hugo de Carvalho ◽  
Robson Luiz Moreno ◽  
Tales Cleber Pimenta ◽  
Paulo C. Crepaldi ◽  
Evaldo Cintra
Keyword(s):  
Heart Disease ◽  
Embedded System ◽  
Cluster Algorithm ◽  
Fuzzy Cluster ◽  
Fuzzy Cluster Algorithm

A mixed attributes oriented dynamic SOM fuzzy cluster algorithm for mobile user classification

2020 ◽  
Vol 515 ◽  
pp. 280-293 ◽  
Author(s):  
Guangxia Xu ◽  
Linghao Zhang ◽  
Chuang Ma ◽  
Yanbing Liu
Keyword(s):  
Cluster Algorithm ◽  
Mobile User ◽  
Fuzzy Cluster ◽  
User Classification ◽  
Fuzzy Cluster Algorithm

Research on the Clustering Color Separation of Fabric Based on Genetic-Fuzzy Algorithm

2011 ◽  
Vol 331 ◽  
pp. 77-82
Author(s):  
Bing Jin Luo
Keyword(s):  
Fuzzy Clustering ◽  
Cluster Algorithm ◽  
Fuzzy Cluster ◽  
Separation Method ◽  
Experimental Results ◽  
Improved Method ◽  
Data Set ◽  
Fuzzy Algorithm ◽  
Color Separation ◽  
Fuzzy Cluster Algorithm

An improved fabric color separation method is put forward based on genetic fuzzy clustering algorithm.At first, adopt six basic primary colors are used to carry on color separation to images of fabric including C(blue), M(magenta), Y(yellow), G(green), K(black) and W(white),and the data set of fuzzy cluster is formed , consequently, the division of fuzzy cluster can be completed, at last Genetic-fuzzy cluster algorithm is applied to color separation of decorated Jacquard fabric. The experimental results show that the fidelity of fabric patterns after color separation using this improved method is superior to that after color separation using traditional color separation method

scholarly journals A Dynamic Fuzzy Cluster Algorithm for Time Series

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Min Ji ◽  
Fuding Xie ◽  
Yu Ping
Keyword(s):  
Time Series ◽  
Cluster Algorithm ◽  
Fuzzy Cluster ◽  
Excellent Performance ◽  
Fuzzy Cluster Algorithm ◽  
Simulation Based ◽  
Class Labels ◽  
Definition Of ◽  
Clustering Problems ◽  
Over Time

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.

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