Efficiency of k-Means and K-Medoids Algorithms for Clustering Arbitrary Data Points

There are number of techniques proposed by several researchers to analyze the performance of clustering algorithms in data mining. All these techniques are not suggesting good results for the chosen data sets and for the algorithms in particular. Some of the clustering algorithms are suit for some kind of input data. This research work uses arbitrarily distributed input data points to evaluate the clustering quality and performance of two of the partition based clustering algorithms namely kMeans and k-Medoids. To evaluate the clustering quality, the distance between two data points are taken for analysis. The computational time is calculated for each algorithm in order to measure the performance of the algorithms. The experimental results show that the k-Means algorithm yields the best results compared with k-Medoids algorithm.