blockcluster: An R Package for Model-Based Co-Clustering
blockcluster: An R Package for Model-Based Co-Clustering
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DOI：
10.18637/jss.v076.i09
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发表时间：
2017-02-01
影响因子：
5.8
通讯作者：
Govaert, Gerard
中科院分区：
文献类型：
--
作者：
Bhatia, Parmeet Singh;Iovleff, Serge;Govaert, Gerard
行列同时聚类是双向数据分析中的一种重要方法，通常称为双聚类、共聚类或块聚类。最近提出了一种新的标准和有效的方法的基础上的潜在块模型（Govaert和Nadif 2003年），它考虑到块聚类问题的个人和变量集。本文介绍了我们的R包blockcluster，用于基于这些模型的二进制，偶然性和连续数据的联合聚类。在本文中，我们将简要回顾基于模型的块聚类方法，并展示如何使用R包blockcluster进行联合聚类。
Simultaneous clustering of rows and columns, usually designated by bi-clustering, co-clustering or block clustering, is an important technique in two way data analysis. A new standard and efficient approach has been recently proposed based on the latent block model (Govaert and Nadif 2003) which takes into account the block clustering problem on both the individual and variable sets. This article presents our R package blockcluster for co-clustering of binary, contingency and continuous data based on these very models. In this document, we will give a brief review of the model-based block clustering methods, and we will show how the R package blockcluster can be used for co-clustering.