Add to ORKGInternational audienceSimultaneous clustering methods perform clustering in the two dimensions simultaneously. They seek to find sub-matrices, that is subgroups of rows and subgroups of columns. They have practical importance in a wide of variety of applications such as biology data analysis, text mining and web mining. In this paper, we introduce a large number of existing approaches of simultaneous clustering and classify them in accordance with the type of biclusters they can find, the methods used and the target applications
International audienceThis paper introduces hard clustering algorithms that are able to partition ob...
Biclustering, which can be defined as the simultaneous clustering of rows and columns in a data matr...
International audienceCo-clustering, that is partitioning a numerical matrix into " homogeneous " su...
International audienceSimultaneous clustering methods perform clustering in the two dimensions simul...
Although most of the clustering literature focuses on one-sided clustering algorithms, simultaneous ...
National audienceCo-clustering aims to identify block patterns in a data table, from a joint cluster...
Biclustering is a technique used to simultaneously cluster both the rows and columns of a data matri...
A large number of clustering approaches have been proposed for the analysis of synthetic datasets ob...
National audienceCo-clustering aims to identify block patterns in a data table, from a joint cluster...
Abstract: One of the major problems in clustering is the need of specifying the optimal number of cl...
Many data types arising from data mining applications can be modeled as bipartite graphs, examples i...
This book summarizes the state-of-the-art in partitional clustering. Clustering, the unsupervised cl...
Cluster or co-cluster analyses are important tools in a variety of scientific areas. The introductio...
Most classical approaches for two-mode clustering of a data matrix are designed to attain homogeneou...
Biclustering can be defined as the simultaneous clustering of rows and columns in a data matrix and ...
International audienceThis paper introduces hard clustering algorithms that are able to partition ob...
Biclustering, which can be defined as the simultaneous clustering of rows and columns in a data matr...
International audienceCo-clustering, that is partitioning a numerical matrix into " homogeneous " su...
International audienceSimultaneous clustering methods perform clustering in the two dimensions simul...
Although most of the clustering literature focuses on one-sided clustering algorithms, simultaneous ...
National audienceCo-clustering aims to identify block patterns in a data table, from a joint cluster...
Biclustering is a technique used to simultaneously cluster both the rows and columns of a data matri...
A large number of clustering approaches have been proposed for the analysis of synthetic datasets ob...
National audienceCo-clustering aims to identify block patterns in a data table, from a joint cluster...
Abstract: One of the major problems in clustering is the need of specifying the optimal number of cl...
Many data types arising from data mining applications can be modeled as bipartite graphs, examples i...
This book summarizes the state-of-the-art in partitional clustering. Clustering, the unsupervised cl...
Cluster or co-cluster analyses are important tools in a variety of scientific areas. The introductio...
Most classical approaches for two-mode clustering of a data matrix are designed to attain homogeneou...
Biclustering can be defined as the simultaneous clustering of rows and columns in a data matrix and ...
International audienceThis paper introduces hard clustering algorithms that are able to partition ob...
Biclustering, which can be defined as the simultaneous clustering of rows and columns in a data matr...
International audienceCo-clustering, that is partitioning a numerical matrix into " homogeneous " su...