Model-based Clustering

Abstract

Mixture models extend the toolbox of clustering methods available to the data analyst. They allow for an explicit definition of the cluster shapes and structure within a probabilistic framework and exploit estimation and inference techniques available for statistical models in general. In this chapter an introduction to cluster analysis is provided, model-based clustering is related to standard heuristic clustering methods and an overview on different ways to specify the cluster model is given. Post-processing methods to determine a suitable clustering, infer cluster distribution characteristics and validate the cluster solution are discussed. The versatility of the model-based clustering approach is illustrated by giving an overview on the different areas of applications.

This is a preprint of a chapter forthcoming in Handbook of Mixture Analysis, edited by Gilles Celeux, Sylvia Frühwirth-Schnatter, and Christian P. Robert

References in corpus (2)

• Bayesian Hierarchical Mixtures of Experts
• Non-Normal Mixtures of Experts

Cited by in corpus (35)

• SPICY: The Spitzer/IRAC Candidate YSO Catalog for the Inner Galactic Midplane
• Approximation by finite mixtures of continuous density functions that vanish at infinity
• Significance Mode Analysis (SigMA) for hierarchical structures. An application to the Sco-Cen OB association
• Consistency of Variational Bayes Inference for Estimation and Model Selection in Mixtures
• On off-line and on-line Bayesian filtering for uncertainty quantification of structural deterioration
• An evaluation framework for synthetic data generation models
• Hierarchical clustering with discrete latent variable models and the integrated classification likelihood
• Spying on the prior of the number of data clusters and the partition distribution in Bayesian cluster analysis
• Selecting the number of clusters, clustering models, and algorithms. A unifying approach based on the quadratic discriminant score
• JAGS, NIMBLE, Stan: a detailed comparison among Bayesian MCMC software
• A Multivariate Extreme Value Theory Approach to Anomaly Clustering and Visualization
• A Census of Above-Horizontal-Branch Stars in Galactic Globular Clusters
• Fast Deep Mixtures of Gaussian Process Experts
• Small Area Estimation of Inequality Measures using Mixtures of Beta
• Optimal Bayesian estimation of Gaussian mixtures with growing number of components
• An Adaptive EM Accelerator for Unsupervised Learning of Gaussian Mixture Models
• COVID-19 Clinical footprint to infer about mortality
• Partitioning the Galactic Halo with Gaussian Mixture Models
• Burglary in London: Insights from Statistical Heterogeneous Spatial Point Processes
• Estimation of Switched Markov Polynomial NARX models
• A Bayesian Fisher-EM algorithm for discriminative Gaussian subspace clustering
• Directional data analysis using the spherical Cauchy and the Poisson kernel-based distribution
• Simulation-based inference for stochastic nonlinear mixed-effects models with applications in systems biology
• Graph-Aligned Random Partition Model (GARP)
• Bayesian mixture models (in)consistency for the number of clusters
• Model based clustering of multinomial count data
• Morpho-statistical characterisation of the spatial galaxy distribution through Gibbs point processes
• Mixture Models and Networks -- Overview of Stochastic Blockmodelling
• Clustering Data with Nonignorable Missingness using Semi-Parametric Mixture Models
• Fast Incremental Expectation Maximization for finite-sum optimization: nonasymptotic convergence
• BNPdensity: Bayesian nonparametric mixture modeling in R
• Bayesian Finite Mixture Models
• Where are the vulnerable children? Identification and comparison of clusters of young children with health and developmental vulnerabilities across Queensland
• Least squares moment identification of binary regression mixtures models
• Identification of Probability weighted ARX models with arbitrary domains