Degree of Match-Based Hierarchical Clustering Technique for Efficient Service Discovery

  • P. Premalatha
  • S. Subasree
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 645)


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.


Degree of Matching-Hierarchical Clustering Technique (DoM-HCT) Service discovery Inter-Cluster Distance (ICD) Threshold-ICD Output Similarity Model (OSM) and Total Similarity Model (TSM) 


  1. 1.
    Sangers, J., Frasincar, F., Hogenboom, F., Chepegin, V.: Semantic web service discovery using natural language processing techniques. Expert Syst. Appl. 40, 4660–4671 (2013)CrossRefGoogle Scholar
  2. 2.
    Kotekar, S., Kamath, S.S.: Enhancing service discovery using cat swarm optimisation based web service clustering. Perspect. Sci. 8, 715–717 (2016)CrossRefGoogle Scholar
  3. 3.
    Du, Y., Gai, J., Zhou, M.: A web service substitution method based on service cluster nets. Enterp. Inf. Syst. 1–17 (2016)Google Scholar
  4. 4.
    Cong, Z., Fernandez, A., Billhardt, H., Lujak, M.: Service discovery acceleration with hierarchical clustering. Inf. Syst. Front. 17, 799–808 (2015)CrossRefGoogle Scholar
  5. 5.
    Aznag, M., Quafafou, M., Jarir, Z.: Leveraging formal concept analysis with topic correlation for service clustering and discovery. In: IEEE International Conference on Web Services (ICWS), pp. 153–160 (2014)Google Scholar
  6. 6.
    Wu, J., Chen, L., Zheng, Z., Lyu, M.R., Wu, Z.: Clustering web services to facilitate service discovery. Knowl. Inf. Syst. 38, 207–229 (2014)CrossRefGoogle Scholar
  7. 7.
    Chen, X., Zheng, Z., Liu, X., Huang, Z., Sun, H.: Personalized qos-aware web service recommendation and visualization. IEEE Trans. Serv. Comput. 6, 35–47 (2013)CrossRefGoogle Scholar
  8. 8.
    Kumara, B.T., Paik, I., Chen, W., Ryu, K.H.: Web service clustering using a hybrid term-similarity measure with ontology learning. Int. J. Web Serv. Res. (IJWSR) 11, 24–45 (2014)CrossRefGoogle Scholar
  9. 9.
    Rupasingha, R.A., Paik, I., Kumara, B.T., Siriweera, T.A.S.: Domain-aware web service clustering based on ontology generation by text mining. In: IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 1–7 (2016)Google Scholar
  10. 10.
    Tian, G., Sun, C., He, K.-Q., Ji, X.-M.: Transferring auxiliary knowledge to enhance heterogeneous web service clustering. Int. J. High Perform. Comput. Netw. 9, 160–169 (2016)CrossRefGoogle Scholar
  11. 11.
    Cheng, B., Li, C., Chen, J.: A web services discovery approach based on interface underlying semantics mining. In: IEEE Transactions on Knowledge and Data Engineering (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Bharathiar UniversityCoimbatoreIndia
  2. 2.Department of Computer Science & Engineering and Information TechnologyNehru College of Engineering and Research CenterCoimbatoreIndia

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