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Bradley. P.S., Fayyad, U.M. and Reina, C.A. (1998) Scaling Clustering Algorithms to Large Databases. Proceedings of the 4th International Conference on Knowledge Discovery & Data Mining, AAAI Press, Menlo Park, CA, 9-15 .

has been cited by the following article:

• TITLE: A State of Art Analysis of Telecommunication Data by k-Means and k-Medoids Clustering Algorithms

  AUTHORS: T. Velmurugan

  KEYWORDS: k-Means Algorithm, k-Medoids Algorithm, Data Clustering, Time Complexity, Telecommunication Data

  JOURNAL NAME: Journal of Computer and Communications, Vol.6 No.1, December 29, 2017

  ABSTRACT: Cluster analysis is one of the major data analysis methods widely used for many practical applications in emerging areas of data mining. A good clustering method will produce high quality clusters with high intra-cluster similarity and low inter-cluster similarity. Clustering techniques are applied in different domains to predict future trends of available data and its uses for the real world. This research work is carried out to find the performance of two of the most delegated, partition based clustering algorithms namely k-Means and k-Medoids. A state of art analysis of these two algorithms is implemented and performance is analyzed based on their clustering result quality by means of its execution time and other components. Telecommunication data is the source data for this analysis. The connection oriented broadband data is given as input to find the clustering quality of the algorithms. Distance between the server locations and their connection is considered for clustering. Execution time for each algorithm is analyzed and the results are compared with one another. Results found in comparison study are satisfactory for the chosen application.