R package blockcluster. Coclustering Package for Binary, Categorical, Contingency and Continuous Data-Sets. Simultaneous clustering of rows and columns, usually designated by biclustering, co-clustering or block clustering, is an important technique in two way data analysis. It consists of estimating a mixture model which takes into account the block clustering problem on both the individual and variables sets. The blockcluster package provides a bridge between the C++ core library and the R statistical computing environment. This package allows to co-cluster binary, contingency, continuous and categorical data-sets. It also provides utility functions to visualize the results. This package may be useful for various applications in fields of Data mining, Information retrieval, Biology, computer vision and many more. More information about the project and comprehensive tutorial can be found on the link mentioned in URL.
Keywords for this software
References in zbMATH (referenced in 8 articles , 1 standard article )
Showing results 1 to 8 of 8.
- Makhalova, Tatiana; Trnecka, Martin: From-below Boolean matrix factorization algorithm based on MDL (2021)
- Selosse, Margot; Jacques, Julien; Biernacki, Christophe: Model-based co-clustering for mixed type data (2020)
- Biernacki, Christophe; Lourme, Alexandre: Unifying data units and models in (co-)clustering (2019)
- François Role, Stanislas Morbieu, Mohamed Nadif: CoClust: A Python Package for Co-Clustering (2019) not zbMATH
- Jacques, Julien; Biernacki, Christophe: Model-based co-clustering for ordinal data (2018)
- Martella, Francesca; Alfò, Marco: A finite mixture approach to joint clustering of individuals and multivariate discrete outcomes (2017)
- Parmeet Bhatia and Serge Iovleff and Gérard Govaert: blockcluster: An R Package for Model-Based Co-Clustering (2017) not zbMATH
- Schepers, Jan; Bock, Hans-Hermann; Van Mechelen, Iven: Maximal interaction two-mode clustering (2017)