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融合KNN优化的密度峰值和FCM聚类算法
引用本文:兰红,黄敏. 融合KNN优化的密度峰值和FCM聚类算法[J]. 计算机工程与应用, 2021, 57(9): 81-88. DOI: 10.3778/j.issn.1002-8331.2005-0011
作者姓名:兰红  黄敏
作者单位:江西理工大学 信息工程学院,江西 赣州 341000
基金项目:江西省自然科学基金;江西省教育厅科技重点项目;国家自然科学基金
摘    要: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...

关 键 词:模糊C均值  聚类  密度峰值  K近邻  算法优化

Fusion of KNN Optimized Density Peaks and FCM Clustering Algorithm
LAN Hong,HUANG Min. Fusion of KNN Optimized Density Peaks and FCM Clustering Algorithm[J]. Computer Engineering and Applications, 2021, 57(9): 81-88. DOI: 10.3778/j.issn.1002-8331.2005-0011
Authors:LAN Hong  HUANG Min
Affiliation:School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China
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.
Keywords:Fuzzy C-Means(FCM)  clustering  density peaks  K Nearest Neighbor(KNN)  algorithm optimization  
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