A Service Clustering Method Based on Wisdom of Crowds

Abstract

As the number and variety of services increase, it is becoming difficult and time-consuming to locate services that satisfy users' need. Service clustering is efficacious method to prune the query space, to narrow the searching space, and improve the accuracy of locating services that satisfied users' needs. At present, clustering method of web services adopted single or traditional clustering algorithms. However, accuracy and stability of single or traditional clustering algorithms is poor. In the paper, we proposed SWOC a service clustering method based on wisdom of crowd. Firstly, by using SWOC we calculated document similarity. Secondly, we implemented a mapping algorithm that reduces the correlation of web services and improve accuracy of method. And then, we applyed different number of clusters using different individual clustering methods that increase the number of partitions so as to enhance the robustness of SWOC. Lastly, the diversity algorithm evaluates and selects the partitions to extract interesting information for the final aggregation with the weight of each individual result. Experiments were performed on the real web service dataset crawled from ProgrammableWeb which prove the accuracy, recall, F-value and stability of proposed method.