Service clustering method based on description context feature words and improved GSDMM model

Abstract

To address the problem that current service clustering methods usually faced low quality of service representation vectors, a service clustering method based on description context feature words and improved GSDMM model was proposed.Firstly, a feature word extraction method based on context weight was constructed.The words that fit well with the context of service description were extracted as the set of feature words for each service.Then, an improved GSDMM model with topic distribution probability correction factor was established to generate service representation vectors and achieve distribution probability correction for non-critical topic items.Finally, K-means++ algorithm was employed to cluster Web services based on these service representation vectors.Experiments were conducted on real Web services in Web site of Programmable Web.Experiment results show that the quality of service representation vectors generated by the proposed method is higher than of other topic models.Further, the performance of our clustering method is significantly better than other service clustering methods.

Keywords

• Web service;service clustering;topic model;GSDMM