Cluster or co-cluster analyses are important tools in a variety of scientific areas. The introduction of this book presents a state of the art of already well-established, as well as more recent methods of co-clustering. The authors mainly deal with the two-mode partitioning under different approaches, but pay particular attention to a probabilistic approach. Chapter 1 concerns clustering in general and the model-based clustering in particular. The authors briefly review the classical clustering methods and focus on the mixture model. They present and discuss the use of different mixtur
A new approach to clustering multivariate data, based on a multilevel linear mixed model, is propose...
Simultaneous clustering of rows and columns, usually designated by bi-clustering, coclustering or bl...
Co-clustering is used to analyze the row and column clusters of a dataset, and it is widely used in ...
Model-based co-clustering can be seen as a particularly valuable extension of model-based clustering...
Co-clustering aims to identify block patterns in a data table, from a joint clustering of rows and c...
The relations between automatic clustering methods and inferentiel statistical models have mostely ...
Handbook of Cluster Analysis provides a comprehensive and unified account of the main research devel...
We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional ...
This book summarizes the state-of-the-art in partitional clustering. Clustering, the unsupervised cl...
In this paper, we present a generative model for co-clustering and develop algorithms based on the m...
Handbook of Cluster Analysis provides a comprehensive and unified account of the main research devel...
Co-clustering is the simultaneous partitioning of the rows and columns of a matrix such that the blo...
International audienceCo-clustering is a data mining technique used to extract the underlying block ...
International audienceCo-clustering is a data mining technique used to extract the underlying block ...
We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional ...
A new approach to clustering multivariate data, based on a multilevel linear mixed model, is propose...
Simultaneous clustering of rows and columns, usually designated by bi-clustering, coclustering or bl...
Co-clustering is used to analyze the row and column clusters of a dataset, and it is widely used in ...
Model-based co-clustering can be seen as a particularly valuable extension of model-based clustering...
Co-clustering aims to identify block patterns in a data table, from a joint clustering of rows and c...
The relations between automatic clustering methods and inferentiel statistical models have mostely ...
Handbook of Cluster Analysis provides a comprehensive and unified account of the main research devel...
We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional ...
This book summarizes the state-of-the-art in partitional clustering. Clustering, the unsupervised cl...
In this paper, we present a generative model for co-clustering and develop algorithms based on the m...
Handbook of Cluster Analysis provides a comprehensive and unified account of the main research devel...
Co-clustering is the simultaneous partitioning of the rows and columns of a matrix such that the blo...
International audienceCo-clustering is a data mining technique used to extract the underlying block ...
International audienceCo-clustering is a data mining technique used to extract the underlying block ...
We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional ...
A new approach to clustering multivariate data, based on a multilevel linear mixed model, is propose...
Simultaneous clustering of rows and columns, usually designated by bi-clustering, coclustering or bl...
Co-clustering is used to analyze the row and column clusters of a dataset, and it is widely used in ...