"Model-based clustering assumes that the data were generated by a model and tries to recover the original model from the data. The model that we recover from the data then defines clusters and an assignment of documents to clusters. A commonly used criterion for estimating the model parameters is maximum likelihood.nlp.stanford.edu › IR-book › html › htmledition › model-based-clusteri...
Model-based clustering - Stanford NLP Group
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Mixture models are also known as model-based clustering. Model-based clustering is a broad family of algorithms designed for modelling an unknown distribution as a mixture of simpler distributions, sometimes called basis distributions.
www.sciencedirect.com › topics › medicine-and-dentistry › model-based...
Model-Based Clustering - an overview | ScienceDirect Topics
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Mixture model
Description
In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observation belongs. Wikipedia
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Introduction to Mixture Models
https://stephens999.github.io/fiveMinuteStats/intro_to_mixture_models.html
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