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Following Hartigan [1975], a cluster is defined as a connected component of the t-level set of the underlying density, i.e., the set of points for which the density is greater than t. A clustering a...
We consider the problem of estimating the number of components and the rel- evant variables in a mixture model for multilocus genotypic data. A new pe- nalized maximum likelihood criterion is propos...
In the context of clustering, we assume a generative model where each cluster is the result of sampling points in the neighborhood of an embedded smooth surface, possibly contaminated with outliers....
Because of the huge number of partitions of even a moderately sized dataset, even when Bayes factors have a closed form, in model-based clustering a comprehensive search for the highest sco...
In this paper we address the problem of obtaining a single clustering estimate c based on an MCMC sample of clusterings c(1), c(2) . . . , c(M) from the posterior distribution of a Bayesi...
This paper defines a class of univariate product partition models for which a novel deterministic search algorithm is guaranteed to find the maximum aposteriori (MAP)clustering or the maximum likeli...
We discuss a model-based approach to identifying clusters of objects based on subsets of attributes, so that the attributes that distinguish a cluster from the rest of the population may depend on t...
Testing for Differences Among Discrete Distributions: An Application of Model-Based Clustering
Spatial interaction between two or more classes or species has important implications in various fields and causes multivariate patterns such as segregation or association. Segregation occurs when mem...
We construct a framework for studying clustering algorithms, which includes two key ideas: persistence and functoriality. The first encodes the idea that the output of a clustering scheme should carr...

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