MapReduce4CFBA: Distributed Incremental Closeness Factor Based Clustering Algorithm (DICFBA) for Analysis of Chronic Diseases on Hadoop MapReduce

Abstract

Mining of items is a key activity in data clustering. Now-a-days databases and related computations go far-flung from capabilities of a solitary machine. Modern studies show implementation of traditional clustering algorithms for parallel paradigms such as MapReduce. Incremental database update initiates need for MapReduce variant of these traditional algorithms. This paper discusses MapReduce implementation of a novel incremental data clustering algorithm called as MapReduce for Closeness Factor based Clustering Algorithm or MapReduce4CFBA. The key part here is design of Mapper, main program for incremental architectures of CFBA. MapReduce4CFBA proved to be effectual for analysis of chronic diseases datasets.