融合KNN优化的密度峰值和FCM聚类算法

针对模糊C均值(Fuzzy C-Means,FCM)聚类算法对初始聚类中心和噪声敏感、对边界样本聚类不够准确且易收敛于局部极小值等问题,提出了一种K邻近(KNN)优化的密度峰值(DPC)算法和FCM相结合的融合聚类算法(KDPC-FCM).算法利用样本的K近邻信息定义样本局部密度,快速准确搜索样本的密度峰值点样本作为初...

Fusion of KNN Optimized Density Peaks and FCM Clustering Algorithm

Aiming at the problems that Fuzzy C-Means（FCM） clustering algorithm is sensitive to the initial clustering center and noise, is not accurate to boundary sample clustering and is easy to converge to the local minimum, a fusion clustering algorithm（KDPC-FCM） combining K Nearest Neighbor（KNN） optimized Density Peaks Clustering（DPC） algorithm and FCM is proposed. The algorithm uses the KNN information of the sample to define the local density of the sample, quickly and accurately searches the sample of the density peak point of the sample as the initial cluster center, and improves the shortcomings of the FCM clustering algorithm, so as to optimize the effect of the FCM clustering algorithm. The experimental results on multiple UCI data sets, a single man-made data set, multiple benchmark data sets, and 6 large-scale data sets in the Geolife project show that compared with the traditional FCM algorithm, and DSFCM algorithm, the improved new algorithm has better noise immunity, clustering effect and faster global convergence speed, which proves the feasibility and effectiveness of the new algorithm.