Model-based Clustering

2018/07/05 by Bettina Grün, Grün, Bettina · 3 citations
Computer Science · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Methodology (stat.ME)

paper · doi:10.48550/arxiv.1807.01987

openalex publication_date 2018/07/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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• Lower-dimensional posterior density and cluster summaries for overparameterized Bayesian models
• Effective regions and kernels in continuous sparse regularisation, with application to sketched mixtures
• Modifications of the BIC for order selection in finite mixture models

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