A strong reinforcement parallel implementation of k-means algorithm using message passing interface

Abstract

In Data analysis process clustering is one of the standard methods, which is used in many area such as pattern recognition and image segmentation and statistics, bioinformatics etc. Clustering have several algorithms in that most well know simplest algorithm is K-means, because of its simplicity, empirical success and efficiency. This present reality applications produce tremendous volumes of data, subsequently, how to productively deal with these data in a significant mining task has been a difficult and huge issue. Likewise Message passing Interface (MPI) as a Programming model for increases the scalability, performance and execution speed. Enthused by this, MPI with strong reinforcement parallel K- means clustering algorithm is implemented in this paper. The efficiency of the strong reinforcement K means clustering algorithm is improved in parallel environment by implementing in MPI methodology. In this paper performance of the clustering the data using K- means is compared between sequentially run of K- means algorithm and parallel run of K-means algorithm in Message passing interface architecture in terms of overhead cost and execution.