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Journal of Classification
Erschienen in: Journal of Classification 1/2022

22.11.2021

High-Dimensional Clustering via Random Projections

verfasst von: Laura Anderlucci, Francesca Fortunato, Angela Montanari

Erschienen in: Journal of Classification | Ausgabe 1/2022

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Abstract

This work addresses the unsupervised classification issue for high-dimensional data by exploiting the general idea of Random Projection Ensemble. Specifically, we propose to generate a set of low-dimensional independent random projections and to perform model-based clustering on each of them. The top B∗ projections, i.e., the projections which show the best grouping structure, are then retained. The final partition is obtained by aggregating the clusters found in the projections via consensus. The performances of the method are assessed on both real and simulated datasets. The obtained results suggest that the proposal represents a promising tool for high-dimensional clustering.
Vorheriger Artikel Chimeral Clustering

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1
See the R Documentation for the sort function with default settings.
 
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Metadaten
Titel
High-Dimensional Clustering via Random Projections
verfasst von
Laura Anderlucci
Francesca Fortunato
Angela Montanari
Publikationsdatum
22.11.2021
Verlag
Springer US
Erschienen in
Journal of Classification / Ausgabe 1/2022
Print ISSN: 0176-4268
Elektronische ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-021-09403-7

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