基于模糊分区聚类的社交网络用户情景模式预测

Prediction of user scenario patterns in social networks based on fuzzy partition clustering

为了提高社交网络用户行为分析和情景模式预测能力,优化社交网络建设,结合数据挖掘和行为分析方法进行社交网络的用户特征分析和用户情景模式的优化挖掘,发现社交网络用户行为特征。提出一种基于模糊分区聚类的社交网络用户情景模式预测方法,构建社交网络用户情景模式分布的关联拓扑结构模型,采用Parallel Sets变元轴排序方法进行社交网络用户情景模式存储结构分区调度,结合分段特征提取方法进行社交网络用户情景模式的关联数据挖掘,采用自适应寻优方法求取社交网络用户的情景模式的分布信息,利用模糊分区聚类方法发现用户情景模式数据集中的隐含模式,根据数据模糊分区聚类和挖掘结果,实现社交网络用户情景模式的自适应预测。仿真结果表明,采用该方法进行社交网络用户情景模式预测的准确性较高,提高了对社交网络用户情景模式特征配准的精度,算法处理的实时性较好。

In order to improve the ability of social network user behavior analysis and scenario pattern prediction,and optimize the construction of social network,combining the methods of data mining and behavior analysis,the user characteristics analysis and user scenario pattern optimization mining of social network are carried out.The behavior characteristics of social network users could be discovered.In this paper,a prediction method of social network user scenario pattern based on fuzzy partition clustering is proposed,and the associated topology model of social network user scenario pattern distribution is constructed.The Parallel Sets argument axis sorting method is used to implement the partition scheduling of the storage structure of the social network user scenario pattern,and the segmented feature extraction method is used to mine the associated data of the social network user scenario pattern.The adaptive optimization method is used to obtain the distribution information of the social network user scenario pattern,the fuzzy partition clustering method is used to find the hidden patterns in the user scenario pattern data set,and according to the data fuzzy partition clustering and mining results,the adaptive prediction of social network user scenarios is realized.The simulation results show that the proposed method has a high accuracy in predicting social network user scenarios,improves the accuracy of feature registration of social network user scenarios,and the algorithm can deal with the real-time performance better.