关键词: 模糊C均值, 聚类, 密度峰值, K近邻, 算法优化

Abstract:

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.

Key words: Fuzzy C-Means（FCM）, clustering, density peaks, K Nearest Neighbor（KNN）, algorithm optimization