Improving Efficiency of K-Means Algorithm for Large Datasets

Abstract

Clustering is a process of grouping objects into different classes based on their similarities. K-means is a widely studied partitional based algorithm. It is reported to work efficiently for small datasets; however the performance is not very appreciable in terms of time of computation for large datasets. Several modifications have been made by researchers to address this issue. This paper proposes a novel way of handling the large datasets using K-means in a distributed manner to obtain efficiency. The concept of parallel processing is exploited by dividing the datasets to a number of baskets and then applying K-means in parallel manner to each such basket. The proposed BasketK-means provides a very competitive performance with considerably less computation time. The simulation results on various real datasets and synthetic datasets presented in the work clearly emphasize the effectiveness of the proposed approach.