WS-SCAN: A effective approach for web services clustering

With the rapid growth of available web services developed by different organizations, clustering of web services is required for conveniently managing services such as web services selection, discovery, composition and QoS prediction. However, the traditional clustering approaches have some drawbacks in similarity measuring and information preprocessing. In this paper, a similarity model is presented to measure the similarity between web services. Based on this model, a special preprocessing approach is proposed, which considers the programming style and naming rules. The proposed approach is combined with the SCAN algorithm and evaluated through the planned experiments. The experimental results show that the proposed model and approach can effectively improve clustering of web services and further improve the web service-based applications such as service discovery, composition and QoS prediction.