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.

High-Dimensional Clustering via Random Projections / Anderlucci L.; Fortunato F.; Montanari A.. - In: JOURNAL OF CLASSIFICATION. - ISSN 0176-4268. - ELETTRONICO. - 39:March(2022), pp. 191-216. [10.1007/s00357-021-09403-7]

High-Dimensional Clustering via Random Projections

Anderlucci L.
;
Fortunato F.;Montanari A.
2022

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.
2022
High-Dimensional Clustering via Random Projections / Anderlucci L.; Fortunato F.; Montanari A.. - In: JOURNAL OF CLASSIFICATION. - ISSN 0176-4268. - ELETTRONICO. - 39:March(2022), pp. 191-216. [10.1007/s00357-021-09403-7]
Anderlucci L.; Fortunato F.; Montanari A.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/845112
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