Degree of match-based hierarchical clustering technique for efficient service discovery

Abstract

Clustering is an essential process in discovering services to fulfill the needs of the clients. There are several issues in clustering the services such as fulfilling the client’s need, threshold computation and selecting an appropriate method of threshold calculation, and computing Inter-Cluster Distance (ICD). To resolve these issues, this paper proposes a novel Degree of Match-Hierarchical Clustering Technique (DoM-HCT). This technique utilizes Output Similarity Model (OSM) and Total Similarity Model (TSM) to make the clustering process more efficient. Extra levels are added to the TSM to improve the DoM. Only outputs are used by OSM, whereas both inputs and outputs are used by TSM. The incorrect clustering of services is avoided, and the demands are unaltered, while choosing threshold-ICD. The proposed DoM-HCT yields maximum precision and recall rate than the existing approaches.