MSCA: Mashup Service Clustering Approach Integrating K-Means and Agnes Algorithms
Howto rapidly and accurately select the users' expected Mashup service has become a challenge problem. For Mashup service discovery,it will greatly reduce the space and scope of services searching to perform service clustering technology in advance,resulting in improving the efficiency and precision of Mashup service discovery. This paper proposes a novel Mashup Service Clustering Approach integrating K-Means and Agnes algorithms( MSCA). MSCA,first of all,will expand and rank the tag label of Mashup service. Secondly,it will calculate the Mashup service integration similarity. Thirdly,K-Means algorithm will be applied to clustering the Mashup service similarity matrix,and those Mashup services with the higher similarity will be found and divided them to N atom-clusters,and then Agnes algorithm will be used to performing hierarchical clustering to the N atom-clusters. Finally,13082 Mashup services are crawled from Programmable Web site and regarded as experimental dataset,and the experimental results showthat the average precision rate and recall rate of MSCA increased by 5. 18% and 5. 84% respectively,compared to the traditional Mashup Service Clustering Approach based on K-Means algorithm.