Title :

blockcluster: An R Package for Model Based Co-Clustering

Author(s) :

Bhatia, Parmeet [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Iovleff, Serge [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
MOdel for Data Analysis and Learning [MODAL]
Govaert, Gérard [Auteur]
Heuristique et Diagnostic des Systèmes Complexes [Compiègne] [Heudiasyc]

Journal title :

Journal of Statistical Software

Pages :

24

Publisher :

University of California, Los Angeles

Publication date :

2017

ISSN :

1548-7660

English keyword(s) :

model based clustering
block mixture model
EM and CEM algorithms
simulta-neous clustering
co-clustering
blockcluster

HAL domain(s) :

Statistiques [stat]/Applications [stat.AP]
Statistiques [stat]/Machine Learning [stat.ML]

English abstract : [en]

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 have been ...
Show more >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 have been recently proposed based on latent block model [Govaert and Nadif (2003)] which takes into account the block clustering problem on both the individual and variables sets. This article presents our R package for co-clustering of binary, contingency and continuous data blockcluster 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.Show less >

Language :

Anglais

Popular science :

Non

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL