Evaluation of k-Medoids and Fuzzy C-Means clustering algorithms for clustering telecommunication data
Thambusamy Velmurugan · 2012
Data mining approach and its technology is used to extract the unknown pattern from the large set of data for the business as well as real time applications. This research work deals with two of the most delegated, partition based clustering algorithms in data mining namely k-Medoids and Fuzzy C-Means. These two algorithms are implemented and the performance is analyzed based on their clustering result quality. The connection oriented broad band data is the source of data for this analysis. To test the performance, the distance between the server locations and their connections are taken for clustering. The number of connections in the servers is changed after the clustering process. The run time for each algorithm is analyzed and the results are compared with one another. Finally, the best algorithm is suggested based on their computational time for the chosen telecommunication data.