文章基本信息

标题：blockcluster: An R Package for Model-Based Co-Clustering
本地全文：下载
作者：Parmeet Singh Bhatia ; Serge Iovleff ; Gérard Govaert 等
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2017
卷号：76
期号：1
页码：1-24
语种：English
出版社：University of California, Los Angeles
摘要：Simultaneous clustering of rows and columns, usually designated by bi-clustering, coclustering 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.
关键词：model-based clustering;block mixture model;EM and CEM algorithms;simultaneous clustering;co-clustering;R;blockcluster