关于切换回归的集成模糊聚类算法 GFC

已经有多个方法可用于解决切换回归问题.根据所提出的基于Newton引力定理的引力聚类算法GC,结合模糊聚类算法,进一步提出了新的集成模糊聚类算法 GFC.理论分析表明GFC 能收敛到局部最小.实验结果表明GFC在解决切换回归问题时,比标准模糊聚类算法更有效,特别在收敛速度方面.

An Integrated Fuzzy Clustering Algorithm GFC for Switching Regressions

In order to solve switching regression problems, many approaches have been investigated. In this paper, anintegrated fuzzy clustering algorithm GFC that combines gravity-based clustering algorithm GC with fuzzy clustering is presented. GC, as a new hard clustering algorithm presented here, is based on the well-known Newton's Gravity Law. The theoretic analysis shows that GFC can conve rge to a local minimum of the object function. Experimental results show that GFC for switching regression problems has better performance than standard fuzzy clustering algorithms, especially in terms of convergence speed.