High-Dimensional Clustering via Random Projections

Laura Anderlucci (), Francesca Fortunato () and Angela Montanari ()
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Laura Anderlucci: University of Bologna
Francesca Fortunato: University of Bologna
Angela Montanari: University of Bologna

Journal of Classification, 2022, vol. 39, issue 1, No 11, 216 pages

Abstract: 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.

Keywords: High-dimensional clustering; Random projections; Model-based clustering (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00357-021-09403-7

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