Service Clustering Based on GSDMM Topic Model

Service clustering is an important part of realizing accurate service discovery and efficient service management. Research on how to improve the accuracy and precision of clustering has become the main task in the field of service clustering. The clustering method proposed in this paper first preprocesses the service description text, extracts feature words that have positive significance for category division, and reduces data sparsity; further, uses the GSDMM topic model to generate the corresponding topic vector; finally, through AGNES algorithm performs the final clustering effect analysis on the vector. Experiments have proved that the GSDMM topic model method proposed in this paper is effective for service clustering.