移动互联环境下基于改进模糊聚类理论的用户分群算法

User clustering algorithm based on improved fuzzy clustering theory in mobile Internet environment

在移动互联环境下,依据用户行为规律对业务兴趣相似用户进行分群,可为业务准确推荐和资源有效配置提供有力支撑。因此,提出一种基于改进模糊聚类理论的用户分群算法。首先,分别定义业务兴趣相似度和业务顺序相似度,进而建立用户综合相似度指标;其次,构建基于用户综合相似度的模糊聚类模型,进而采用网格划分方法确定初始群组中心,并依据平均用户隶属度调整用户群组个数,从而实现快速准确的用户分群。仿真结果验证了该算法的有效性。

In mobile Internet environment,users with similar service interests are grouped according to their behavior law,which provides the strong support for accurate service recommendation and effective resource allocation.Therefore,this paper proposed an improved fuzzy clustering theory based user clustering algorithm.First,it respectively defined service interest si-milarity and service sequence similarity,and then established integrated similarity metric.Second,it formulated a integrated similarity metric based fuzzy clustering model,which determined the initial cluster centers by using the grid partition method and adjusted the user cluster number according to the average user membership,so as to obtain user clusters rapidly and accurately.Simulation results verify the effectiveness of proposed algorithm.