Big Numeric Data Classification Using Grid-based Bayesian Inference in the MapReduce Framework
Young Joon Kim, Keon Myung Lee · International Journal of Fuzzy Logic and Intelligent Systems · 2014
In the current era of data-intensive services, the handling of big data is a crucial issue that affects almost every discipline and industry. In this study, we propose a classification method for large volumes of numeric data, which is implemented in a distributed programming framework, i.e., MapReduce. The proposed method partitions the data space into a grid structure and it then models the probability distributions of classes for grid cells by collecting sufficient statistics using distributed MapReduce tasks. The class labeling of new data is achieved by k-nearest neighbor classification based on Bayesian inference.