Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data - T., Velmurugan (Applied soft computing, 2014)
•The partition based clustering algorithms k-Means and Fuzzy C-Means algorithms are taken for analysis via its computational time.•The distance between servers and user connections of telecommunication data are taken for clustering.•The computational time and number of connections in each server was reported by the algorithms after clustering process.•The distribution of data points by k-Means algorithm is even to all the data centers, but, it is not even by the FCM algorithm.•From the experimental analysis, the computational time of k-Means algorithm is less than the FCM algorithm. Data mining is the process of discovering meaningful new correlation, patterns and trends by sifting through large amounts of data, using pattern recognition technologies as well as statistical and mathematical techniques. Cluster analysis is often used as one of the major data analysis technique widely applied for many practical applications in emerging areas of data mining. Two of the most delegated, partition based clustering algorithms namely k-Means and Fuzzy C-Means are analyzed in this research work. These algorithms are implemented by means of practical approach to analyze its performance, based on their computational time. The telecommunication data is the source data for this analysis. The connection oriented broad band data is used to find the performance of the chosen algorithms. The distance (Euclidian distance) between the server locations and their connections are rearranged after processing the data. The computational complexity (execution time) of each algorithm is analyzed and the results are compared with one another. By comparing the result of this practical approach, it was found that the results obtained are more accurate, easy to understand and above all the time taken to process the data was substantially high in Fuzzy C-Means algorithm than the k-Means.