A Parallel EM Algorithm for Model-Based Clustering Applied to the Exploration of Large Spatio-Temporal Data

作     者：Chen, Wei-Chen Ostrouchov, George Pugmire, David Prabhat Wehner, Michael 

作者机构：Univ Tennessee Dept Ecol & Evolutionary Biol Knoxville TN 37996 USA Oak Ridge Natl Lab Comp Sci & Math Div Oak Ridge TN 37831 USA Univ Calif Berkeley Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA 94720 USA 

出 版 物：《TECHNOMETRICS》 (技术计量学)

年 卷 期：2013年第55卷第4期

页      面：513-523页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 

基　　金：Regional and Global Climate Modeling Program of the Office of Biological and Environmental Research in the Department of Energy Office of Science [DE-AC02-05CH11231] Office of Science of the U.S. Department of Energy [DE-AC05-00OR22725] 

主　　题：Parallel computing Parallel coordinate plot Spatial time series Unsupervised learning 

摘      要：We develop a parallel expectation-maximization (EM) algorithm for multivariate Gaussian mixture models and use it to perform model-based clustering of a large climate dataset. Three variants of the EM algorithm are reformulated in parallel and a new variant that is faster is presented. All are implemented using the single program, multiple data programming model, which is able to take advantage of the combined collective memory of large distributed computer architectures to process larger datasets. Displays of the estimated mixture model rather than the data allow us to explore multivariate relationships in a way that scales to arbitrary size data. We study the performance of our methodology on simulated data and apply our methodology to a high-resolution climate dataset produced by the community atmosphere model (CAM5). This article has supplementary material online.