A Novel Approach for PAM Clustering Method

Abstract

Existing and in recent times proposed clustering algorithms are studied and it is known that the k-means clustering method is mostly used for clustering of data due to its reduction of time complexity. But the foremost drawback of k-means algorithm is that it suffers from sensitivity of outliers which may deform the distribution of data owing to the significant values. The drawback of the k-means algorithm is resolved by k-medoids method where the novel approach uses user defined value for k. As a result, if the number of clusters is not chosen suitably, the accuracy will be minimized. Even, K-medoids algorithm does not scale well for huge data set. In order to overcome the above stated limitations, a new grid based clustering method is proposed, where time complexity of proposed algorithm is depending on the number of cells. Simulation results show that, the proposed approach has less time complexity and provides natural clustering method which scales well for large dataset.