Variable selection for clustering with Gaussian mixture models: state of the art

2017/01/31 by Abdelghafour Talibi, Talibi, Abdelghafour, Boujemâa Achchab +3
Computer Science · #Advanced Clustering Algorithms Research #Bayesian Methods and Mixture Models #Data Management and Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.1701.08946

openalex publication_date 2017/01/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The mixture models have become widely used in clustering, given its probabilistic framework in which its based, however, for modern databases that are characterized by their large size, these models behave disappointingly in setting out the model, making essential the selection of relevant variables for this type of clustering. After recalling the basics of clustering based on a model, this article will examine the variable selection methods for model-based clustering, as well as presenting opportunities for improvement of these methods.

Citations

• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Wrappers for feature subset selection

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