Partitional Based Clustering Algorithms on Big Data Using Apache Spark

Abstract

Apache Spark, a framework similar to the Von Neumann architecture. It has an efficient implementation of in-memory computations and iterative optimization is processed to analyze large volume of data. Data captured at high velocity and from variety of different sources known as Big Data. Such big data can be partitioned and clustered, based on parameters of the data. The parameterized clusters are enhanced under clustering algorithms for better outcomes. In this paper the current approach optimize computation over random sampling algorithms, where empirical evidence exhibit the significant change in computation of partition algorithms. Computations can be carried out in iterative procedure for wide variety of datasets retaining an abstraction known as Resilient Distributed Datasets (RDDs).