结合中心约束改进聚类算法的社区发现技术-中国烟草学报-计算机.PDFVIP

Computer Engineering and Applications 计算机工程与应用 2018 ，54（8） 265 结合中心约束改进聚类算法的社区发现技术 用 夏洋洋，刘 渊，黄亚东 应 XIA Yangyang, LIU Yuan, HUANG Yadong 与 江南大学 数字媒体学院，江苏 无锡 214122 程 School of Digital Media, Jiangnan University, Wuxi, Jiangsu 214122, China g 工 r o XIA Yangyang, LIU Yuan, HUANG Yadong. Community discovery based on improved clustering algorithm with . j central constraints. Computer Engineering and Applications, 2018, 54 （8 ）：265-270. 机 a 算 e Abstract ：In the process of community discovery, it firstly starts a random walk from a node, calculates the symmetrical c . social distance between two nodes, and uses this distance to analyze the correlation between two user nodes. In the social 计 w network, there is a phenomenon of non-uniformity. Some individuals are very dense, while others are very sparse. Therefore, the virtual community needs to be excavated with specific community discovery technology. However, through the accuracy w index of virtual community algorithm evaluation, it is found that for the data with large data volume and strong data sticki- w ness, the clustering algorithm of poly- clustering algorithm （PCM ）class effect is not ideal. The PCM algorithm is im- proved with central constraints, the new clustering algorithm is more suitable for the existence of some data missing or there is a large number of noise, the exception point of the real network data set. Experiments are carried out to verify the accuracy of the real data set. Key words ：symmertrical social distance; random walk; P