Analysis and Implementation of Modified K-medoids Algorithm to Increase Scalability and Efficiency for Large Dataset

Clustering plays a vital role in research area in the field of data mining. Clustering is a process of partitioning a set of data in a meaningful sub classes called clusters. It helps users to understand the natural grouping of cluster from the data set. It is unsupervised classification that means it has no predefined classes. Applications of cluster analysis are Economic Science, Document classification, Pattern Recognition, Image Processing, text mining. Hence, in this study some algorithms are presented which can be used according to one’s requirement. K-means is the most popular algorithm used for the purpose of data segmentation. K-means is not very effective in many cases. Also it is not even applicable for data segmentation in some specific kinds of matrices like Absolute Pearson. Whereas K-Medoids is considered flexible than k-means and also carry compatibility to work with almost every type of data matrix. The medoid computed using k-Medoids algorithm is roughly comparable to the median. After checking the literature on median, we have found a number of advantages of median over arithmetic mean. In this paper, we have used a modified version of k-medoids algorithm for the large data sets. Proposed k-medoids algorithm has been modified to perform faster than k-means because speed is the major cause behind the k-medoids unpopularity as compared to kmeans. Our experimental results have shown that improved k-medoid performed better than k-means and k-medoid in terms of cluster quality and elapsed time