Implementation of K-Means Algorithm by Using Map Reduction and Bulk Synchronous Parallelism

Abstract

K-Means algorithm to classify or to group objects based on attributes/features into K number of group. Grouping is done by minimizing the sum of squares of distances between data and the corresponding cluster centroid. The purpose of Kmean clustering is classify the data. Map Reduce is a software framework for easily writing applications which process vast amounts of data. One of the major drawbacks of the Map-Reduce (MR) model is to simplify reliability and fault tolerance. It does not preserve data in memory across consecutive MR jobs. The Bulk Synchronous Parallelism (BSP) programming model, as an alternative to the MR model that does not suffer from this restriction and under certain circumstances allows complex repetitive algorithms to run entirely in the collective memory of a cluster.