【摘 要】
：
For the question that fuzzy c-means(FCM)clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in l
【机 构】
：
Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Int
【出 处】
：
Journal of Shanghai Jiaotong University(Science)
论文部分内容阅读
For the question that fuzzy c-means(FCM)clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in local optima,this paper introduces a new metric norm in FCM and particle swarm optimization(PSO)clustering algorithm,and proposes a parallel optimization algorithm using an improved fuzzy c-means method combined with particle swarm optimization(AF-APSO).The experiment shows that the AF-APSO can avoid local optima,and get the best fitness and clustering performance significantly.
For the question that fuzzy c-means (FCM) clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in local optima, this paper introduces a new metric norm in FCM and particle swarm optimization (PSO) clustering algorithm , and proposes a parallel optimization algorithm using an improved fuzzy c-means method combined with particle swarm optimization (AF-APSO). The experiment shows that the AF-APSO can avoid local optima, and get the best fitness and clustering performance significantly.
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