Implementation of Extended K-Medoids Algorithm to Increase Efficiency and Scalability using Large Datasets
Swarndeep Saket, Sharnil Pandya · International Journal of Computer Applications · 2016
Clustering techniques are application tools to analyze stored data in various fields.Clustering is a process to partition meaningful data into useful clusters which can be understood easily and has analytical value.The K-Means and K-Medoid Algorithms in their existing structure carry certain weaknesses.For example in case of K-Means algorithm "deformation" and "deviations" may arise due to the misbehavior and disruption in the computing process.Similarly in case of K-Medoid Algorithm a lot of iteration is required which consumes huge amount of time and their by reduces the efficiency of clustering.In the present paper, we have proposed a new Modified K-Medoid Algorithm for improving efficiency and scalability for the study of large datasets.The extended K-Medoids Algorithm stand better in terms of execution time, quality of clusters, number of clusters and number of records than the comparative results of K-Means and K-Medoid Algorithm.Extended K-Medoid Algorithm is evaluated using sample real employee datasets and results are compared with K-Means and K-Medoids.