ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2010
Combining Mixture Components for Clustering.
Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
What it found
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The trial behind it
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Who cites it
41 citing papers in PubMed.
- Quantitative 3D Real-Space Analysis of Photonic Supraparticles.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Computationally efficient multi-sample flow cytometry data analysis using Gaussian mixture models.BMC bioinformatics · 2025Article
- Using a Composite Summary of Daily Sex Hormones to Gauge Time Until Menopause: A Focus on Pregnanediol Glucuronide (PDG).The Journal of clinical endocrinology and metabolism · 2025Article
- Subtyping Service Receipt in Personality Disorder Services in South London: Observational Validation Study Using Latent Profile Analysis.Interactive journal of medical research · 2025Article
- Bayesian Clustering via Fusing of Localized Densities.Journal of the American Statistical Association · 2025Article
- Exploring protein-mediated compaction of DNA by coarse-grained simulations and unsupervised learning.Biophysical journal · 2024Article
- Evaluating the Influence of Row Orientation and Crown Morphology on Growth ofPlant phenomics (Washington, D.C.) · 2024Article
- Estimated specific antibody-based true sero-prevalences of canine filariosis in dogs in Central Europe and the UK.Parasitology research · 2022Article
- Monitoring of Hidden Corrosion Growth in Aircraft Structures Based on D-Sight Inspections and Image Processing.Sensors (Basel, Switzerland) · 2022Article
- Machine learning of COVID-19 clinical data identifies population structures with therapeutic potential.iScience · 2022Article
- beeRapp: an R shiny app for automated high-throughput explorative analysis of multivariate behavioral data.Bioinformatics advances · 2022Article
- Summarizing Finite Mixture Model with Overlapping Quantification.Entropy (Basel, Switzerland) · 2021Article
- Predicting Inter-individual Variability During Lipid Resuscitation of Bupivacaine Cardiotoxicity in Rats: A Virtual Population Modeling Study.Drugs in R&D · 2021Article
- Real-Time Point Process Filter for Multidimensional Decoding Problems Using Mixture Models.Journal of neuroscience methods · 2021Article
- How to Use Model-Based Cluster Analysis Efficiently in Person-Oriented Research.Journal for person-oriented research · 2021Article
- Increased Intraspecies Diversity inAntibiotics (Basel, Switzerland) · 2020Article
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- Students' Motivation and Affection Profiles and Their Relation to Mathematics Achievement, Persistence, and Behaviors.Frontiers in psychology · 2020Article
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- Classification of electrophysiological and morphological neuron types in the mouse visual cortex.Nature neuroscience · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Model-based clustering consists of fitting a mixture model to data and identifying each cluster with one of its components. Multivariate normal distributions are typically used. The number of clusters is usually determined from the data, often using BIC. In practice, however, individual clusters can be poorly fitted by Gaussian distributions, and in that case model-based clustering tends to represent one non-Gaussian cluster by a mixture of two or more Gaussian distributions. If the number of mixture components is interpreted as the number of clusters, this can lead to overestimation of the number of clusters. This is because BIC selects the number of mixture components needed to provide a good approximation to the density, rather than the number of clusters as such. We propose first selecting the total number of Gaussian mixture components, K, using BIC and then combining them hierarchically according to an entropy criterion. This yields a unique soft clustering for each number of clusters less than or equal to K. These clusterings can be compared on substantive grounds, and we also describe an automatic way of selecting the number of clusters via a piecewise linear regression fit to the rescaled entropy plot. We illustrate the method with simulated data and a flow cytometry dataset. Supplemental Materials are available on the journal Web site and described at the end of the paper.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.