Spectral Clustering of Web Services by Fusing Document-based and Tag-based Topics Similarity
Liping Deng, Wen Zheng · 2020
This paper proposes a Web services clustering method based on network and integration tags. Firstly, a Document-Tag LDA model(DTag-LDA), is proposed, which considers the tag information of Web services and the tag can describe the effective information of documents accurately. Through the unified modeling of integrated description document and tag information, the web service network is constructed, and then the network is clustered to improve the clustering effect. Based on the first model, we further propose an efficient Document weight and Tag weight-LDA model(DTw-LDA), which fused multi-modal data network. To further improve the clustering accuracy, the model constructs the network for describing text and tag respectively, and then merges the two networks to generate web service network clustered. In addition, we also design experiments to verify that the used auxiliary information can help to extract more accurate semantics by conducting service classification. The proposed model is evaluated on the real-world data-sets and the experimental results show that the accuracy and recall rate are more than 0.7.And the method has obvious advantages in precision, recall, purity and other performance.