Bayesian LDA for mixed-membership clustering analysis: The Rlda package
Bayesian LDA for mixed-membership clustering analysis: The Rlda package
复制标题
DOI：
10.1016/j.knosys.2018.10.024
复制
发表时间：
2019-01-01
影响因子：
8.8
通讯作者：
Li, Daijiang
中科院分区：
文献类型：
--
作者：
Albuquerque, Pedro H. M.;do Valle, Denis Ribeiro;Li, Daijiang
本文的目标是提出基于潜狄利克雷分配模型的混合隶属度聚类分析的Rlda包，该模型适用于不同类型的数据（即多项、伯努利和二项数据）。我们提出了相应的统计模型，并通过使用Rlda包的几个例子说明了它们的使用。因为这些类型的数据经常出现在不同的领域，如生态学、遥感、市场营销和金融，我们相信这个包将会引起对无监督模式识别感兴趣的用户的广泛兴趣，特别是对分类数据的混合成员聚类分析。(C) 2018 Elsevier B.V.版权所有
The goal of this paper is to present the Rlda package for mixed-membership clustering analysis based on the Latent Dirichlet Allocation model adapted to different types of data (i.e., Multinomial, Bernoulli, and Binomial entries). We present the corresponding statistical models and illustrate their use with several examples using the Rlda package. Because these types of data frequently emerge in fields as disparate as ecology, remote sensing, marketing, and finance, we believe this package will be of broad interest for users interested in unsupervised pattern recognition, particularly mixed-membership clustering analysis for categorical data. (C) 2018 Elsevier B.V. All rights reserved.