Comparing K-means, K-medoids and ISODATA Clustering Algorithms for a Cloud Service Search Engine
Walaa Reda1, Hanan Elazhary2, Ehab Hassanein3

1Walaa Reda, Information Systems Department, Cairo University, Cairo, Egypt.
2Hanan Elazhary, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia; Computers and Systems Department, Electronics Research Institute, Cairo, Egypt.
3Ehab Hassanein, Information Systems Department, Cairo University, Cairo, Egypt. 

Manuscript received on 20 August 2019. | Revised Manuscript received on 24 August 2019. | Manuscript published on 30 September 2019. | PP: 4978-4982 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5632098319/2019©BEIESP | DOI: 10.35940/ijrte.C5632.098319
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Abstract: The evolution of cloud computing over the past few years is potentially one of the major advances in the history of computing. Cloud computing theoretically provides all computing needs as services. Accordingly, a large number of cloud service providers exist and the number is constantly increasing. This presents a significant problem for a user to find a relevant service provider, and calls for developing a specialized search engine to help users select suitable services matching their needs. Towards this goal, we developed a search engine that crawls the web sites of various service providers, extracts service attributes from their JavaScript Object Notation (JSON) files and normalizes the attributes in a service table. Those attributes are clustered using one of three different algorithms (K-means, K-medoids, and ISODATA). The requirements of a given user are then matched against the centroids of the various clusters to help obtain the closest match. In this paper, we compared the three algorithms with respect to time and accuracy. The ISODATA algorithm exhibited the best performance.
Keywords: Cloud Computing, Cloud Services, ISODATA, K-means, K-medoids.

Scope of the Article:
Cloud Computing