Improving The Scalability And Efficiency Of K-Medoids By Map Reduce

Mr D Lakshmi Srinivasulu, Mr A Vishnuvardhan Reddy, Dr V S Giridhar Akula

Day to day the size of data increased enormously. Big Data concerns large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity. Big Data is now rapidly expanding in all science and engineering domains, including physical, biological and bio-medical sciences. Mining knowledge from the large amount of data is a challengeable task. Map Reduce is a programming model and an associated implementation for processing and generating large data sets. Map reduce is one of the technique to achieve parallelism. map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key . Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data, scheduling the program’s execution across a set of machines, handling machine failures, and managing the required inter-machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system. In the perspective of clustering, grouping of similar of objects from big data is a challengeable task .In order to deal with the problem; many researchers try to design different parallel clustering algorithms. In this paper, we propose a parallel K-Medoids clustering algorithm to improve scalability without noise and efficiency based on Map Reduce.