融合密度峰值和模糊C-均值聚类算法

Algorithm fuses density peaks and fuzzy C-means clustering

针对模糊C—均值(FCM)聚类算法聚类结果依赖于初始中心的选取,易收敛于局部极值等问题,提出了一种密度峰值聚类(DPC)算法和FCM相结合的混合聚类方法(DPC-FCM),利用密度峰值快速搜索算法可以比较准确地刻画聚类初始中心的特点,改善FCM聚类算法存在的不足,从而实现优化聚类。在UCI数据集和人工模拟数据集上的实验结果显示:融合后的新算法和传统的FCM算法相比有着更高的正确率和更快的收敛速度,证明了新算法的可行性。

Aiming at problem that clustering result of fuzzy C-means（ FCM） clustering algorithm depends on selection of initial center,easy convergence to local minima,a hybrid clustering method combines density peak fast search algorithm（ DPC） with FCM algorithm（ DPC-FCM） is proposed. Algorithm uses DPC algorithm can accurately describe characteristics of initial cluster centers,to improve the shortcomings of the FCM clustering algorithm,so as to realize the optimization of clustering. The experimental results of the UCI data sets and artificial data sets show that the fused new algorithm has a higher accuracy and faster convergence speed,than traditional FCM algorithm thus proves the feasibility of the new algorithm.