mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models

IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
R Journal Pub Date : 2016-08-01 DOI:10.32614/RJ-2016-021
L. Scrucca, Michael Fop, T. B. Murphy, A. Raftery
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引用次数: 1758

Abstract

Finite mixture models are being used increasingly to model a wide variety of random phenomena for clustering, classification and density estimation. mclust is a powerful and popular package which allows modelling of data as a Gaussian finite mixture with different covariance structures and different numbers of mixture components, for a variety of purposes of analysis. Recently, version 5 of the package has been made available on CRAN. This updated version adds new covariance structures, dimension reduction capabilities for visualisation, model selection criteria, initialisation strategies for the EM algorithm, and bootstrap-based inference, making it a full-featured R package for data analysis via finite mixture modelling.
课程5:使用高斯有限混合模型的聚类、分类和密度估计
有限混合模型越来越多地被用于模拟各种随机现象,用于聚类、分类和密度估计。mclust是一个强大而流行的软件包,它允许将数据建模为具有不同协方差结构和不同数量混合成分的高斯有限混合物,用于各种分析目的。最近,该包的第5版已经在CRAN上发布。这个更新版本增加了新的协方差结构、可视化降维能力、模型选择标准、EM算法的初始化策略和基于自引导的推理,使其成为一个功能齐全的R包,可通过有限混合建模进行数据分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
R Journal
R Journal COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍: The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R. The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to: - put their contribution in context, in particular discuss related R functions or packages; - explain the motivation for their contribution; - provide code examples that are reproducible.
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