Due to recent advances in methods and software for model-based clustering, and to the interpretability of the results, clustering procedures based on probability models are increasingly preferred over heuristic methods. The clustering process estimates a model for the data that allows for overlapping clusters, producing a probabilistic clustering that quantifies the uncertainty of observations ...

Clustering can be considered as the most important unsupervised learning problem which deals with finding a structure in a collection of unlabeled data. To this end, it conducts a process of organizing objects into groups whose members are similar in some way and dissimilar to those of other groups [1]. While this process yields in an entirely unsupervised manner, additional background informat...

Symmetric Nonnegative Matrix Factorization (SNMF) models arise naturally as simple reformulations of many standard clustering algorithms including the popular spectral clustering method. Recent work has demonstrated that an elementary instance of SNMF provides superior clustering quality compared to many classic clustering algorithms on a variety of synthetic and real world data sets. In this w...

Bayesian Multi-net (BMN) classifiers consist of several local models, one for each data subset, to model asymmetric, more consistent dependency relationships among variables in each subset. This paper extends an earlier work of ours and proposes several contributions to the field of clustering-based BMN classifiers, using Ant Colony Optimization (ACO). First, we introduce a new medoidbased meth...

Clustering is a fundamental problem in many scientific applications. Standard methods such as k-means, Gaussian mixture models, and hierarchical clustering, however, are beset by local minima, which are sometimes drastically suboptimal. Recently introduced convex relaxations of k-means and hierarchical clustering shrink cluster centroids toward one another and ensure a unique global minimizer. ...

Feature selection has been explored extensively for use in several real-world applications. In this paper, we propose a new method to select a salient subset of features from unlabeled data, and the selected features are then adaptively used to identify natural clusters in the cluster analysis. Unlike previous methods that select salient features for clustering, our method does not require a pr...

We propose a multi-cue based approach for recognizing human actions in still images, where relevant object regions are discovered and utilized in a weakly supervised manner. Our approach does not require any explicitly trained object detector or part/attribute annotation. Instead, a multiple instance learning approach is used over sets of object hypotheses in order to represent objects relevant...

Clustering of the samples is a standard procedure for the analysis of gene expression data, for instance to discover cancer subtypes. However, more than one biologically meaningful clustering can exist, depending on the genes chosen. We propose here to group the genes in function of the clustering of the samples they fit. This allows to determine directly the different clusterings of the sample...

Clustering of the samples is a standard procedure for the analysis of gene expression data, for instance to discover cancer subtypes. However, more than one biologically meaningful clustering can exist, depending on the genes chosen. We propose here to group the genes in function of the clustering of the samples they t. This allows to determine directly the di erent clusterings of the samples p...

In this paper, we propose an automatic PAM (Partition Around Medoids) clustering algorithm for outlier detection. The proposed methodology comprises two phases, clustering and finding outlying score. During clustering phase we automatically determine the number of clusters by combining PAM clustering algorithm and a specific cluster validation metric, which is vital to find a clustering solutio...