An Empirical Study on User Role Discovery Based on Clustering Algorithms and Optimizations in Location-Based Social Network

Location-Based Social Network (LBSN) has been widely used in social lives. Role is an important concept in user's personalized analysis. Many automatic methods such as machine learning method and social network analysis method have been used in user role discovery in LBSN, however, the effectiveness of these methods has not been comprehensively analyzed. In this paper, firstly, the effectiveness of five clustering algorithms is comprehensively analyzed, including K-means algorithm, Bi-Kmeans algorithm, DBSCAN (Density-Based Spatial Clustering Application with Noise) algorithm, OPTICS (Ordering points to identify the clustering structure) algorithm and Agglomerate algorithms. Secondly, four strategies are designed to optimize the algorithm for user role discovery, namely GBK-means algorithm, RDKmeans (Range and density k-means) algorithm, Canopy- based algorithm and reinforcement learning based algorithm. Thirdly, six data sets are used to validate the effectiveness of these algorithms, and the result shows that the optimization strategies are effective.