Unsupervised clustering of utterances can be useful for the modeling of dialogue acts for dialogue applications. Previously, the Chinese restaurant process (CRP), a non-parametric Bayesian method, has been introduced and has shown promising results for the clustering of utterances in dialogue. This paper newly introduces the infinite HMM, which is also a nonparametric Bayesian method, and verif...

Clustering is a fundamental task in many vision applications. To date, most clustering algorithms work in a batch setting and training examples must be gathered in a large group before learning can begin. Here we explore incremental clustering, in which data can arrive continuously. We present a novel incremental model-based clustering algorithm based on nonparametric Bayesian methods, which we...

\Ve consider the problem of color image quantization, or clustering of the color space. vVe propose a new methodology for doing this, called model-based clustering trees. This is grounded in model-based clustering, which bases inference on finite mixture models estimated by maximum likelihood using the EM algorithm, and automatically chooses the number of clusters by Bayesian model selection, a...

Nowadays, the automated text classification has witnessed special importance due to the increasing availability of documents in digital form and ensuing need to organize them. Although this problem is in the Information Retrieval (IR) field, the dominant approach is based on machine learning techniques. Approaches based on classifier committees have shown a better performance than the others. I...

This paper establishes a general framework for Bayesian model-based clustering, in which subset labels are exchangeable, and items are also exchangeable, possibly up to covariate effects. It is rich enough to encompass a variety of existing procedures, including some recently discussed methodologies involving stochastic search or hierarchical clustering, but more importantly allows the formulat...

This paper presents clustering techniques that partition temporal data into homogeneous groups, and constructs state based proles for each group in the hidden Markov model (HMM) framework. We propose a Bayesian HMM clustering methodology that improves upon existing HMM clustering by incorporating HMM model size selection into clustering control structure to derive better cluster models and part...

Document modeling is important for document retrieval and categorization. The probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA) are popular paradigms of document models where word/document correlations are inferred by latent topics. In PLSA and LDA, the unseen words and documents are not explicitly represented at the same time. Model generalization is constrain...

This paper introduces a Bayesian method for clustering dynamic processes and applies it to the characterization of the dynamics of a military scenario. The method models dynamics as Markov chains and then applies an agglomerative clustering procedure to discover the most probable set of clusters capturing the di erent dynamics. To increase e ciency, the method uses an entropy-based heuristic se...

Networks are used in many scientific fields such as biology, social science, and information technology. They aim at modelling, with edges, the way objects of interest, represented by vertices, are related to each other. Looking for clusters of vertices, also called communities or modules, has appeared to be a powerful approach for capturing the underlying structure of a network. In this contex...