A binned technique for scalable model-based clustering on huge datasets

Filippo Antonazzo (1) , Christophe Biernacki (1) , Christine Keribin (2)

1 MODAL - MOdel for Data Analysis and Learning
2 CELESTE - Statistique mathématique et apprentissage

Clustering is impacted by the regular increase of sample sizes which provides opportunity to reveal information previously out of scope. However, the volume of data leads to some issues related to the need of many computational resources and also to high energy consumption. Resorting to binned data depending on an adaptive grid is expected to give proper answer to such green computing issues while not harming the quality of the related estimation. After a brief review of existing methods, a first application in the context of univariate model-based clustering is provided, with a numerical illustration of its advantages. Finally, an initial formalization of the multivariate extension is done, highlighting both issues and possible strategies.

Mots clés: Clustering; binned data; big data; green computing

Domaines: Statistiques [stat]

Filippo Antonazzo, Christophe Biernacki, Christine Keribin. A binned technique for scalable model-based clustering on huge datasets. MBC2 - Models and Learning for Clustering and Classification, Sep 2020, Catania, Italy. ⟨hal-03097284v1⟩