Discrete random probability measures stand out as effective tools for Bayesian clustering. The investigation in the area has been very lively, with a strong emphasis on nonparametric procedures based either Dirichlet process or more flexible generalizations, such normalized independent increments (NRMI). literature finite-dimensional discrete priors is much limited and mostly confined to standa...

The use of mutual information as a similarity measure in agglomerative hierarchical clustering (AHC) raises an important issue: some correction needs to be applied for the dimensionality of variables. In this work, we formulate the decision of merging dependent multivariate normal variables in an AHC procedure as a Bayesian model comparison. We found that the Bayesian formulation naturally shri...

Clustering analysis of the gene expression profiles has been used for identifying the functions of unknown genes. Fuzzy clustering method, which is one category of clustering, assigns one sample to multiple clusters as their degrees of membership. It is more appropriate for analyzing gene expression profiles because genes usually belong to multiple functional families. However, general clusteri...

A nonparametric Bayesian approach to co-clustering ensembles is presented. Similar to clustering ensembles, coclustering ensembles combine various base co-clustering results to obtain a more robust consensus co-clustering. To avoid pre-specifying the number of co-clusters, we specify independent Dirichlet process priors for the row and column clusters. Thus, the numbers of rowand column-cluster...

Feature selection is an important task in clustering problems. Some features help to find useful clusters whereas others may hinder the clustering process. In other words, some selected features can provide better clusters. Besides, the feature selection process also allows the reduction of the dataset dimensionality, improving the clustering method efficiency. This work describes a Bayesian fe...

Abstract Spectral embedding of network adjacency matrices often produces node representations living approximately around low-dimensional submanifold structures. In particular, hidden substructure is expected to arise when the graph generated from a latent position model. Furthermore, presence communities within might generate community-specific structures in embedding, but this not explicitly ...

A Bayesian model-based clustering method is proposed for clustering objects on the basis of dissimilarites. This combines two basic ideas. The first is that the objects have latent positions in a Euclidean space, and that the observed dissimilarities are measurements of the Euclidean distances with error. The second idea is that the latent positions are generated from a mixture of multivariate ...