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High-Dimensional Clustering via Random Projections

Author

Listed:
  • Laura Anderlucci

    (University of Bologna)

  • Francesca Fortunato

    (University of Bologna)

  • Angela Montanari

    (University of Bologna)

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.

Suggested Citation

  • Laura Anderlucci & Francesca Fortunato & Angela Montanari, 2022. "High-Dimensional Clustering via Random Projections," Journal of Classification, Springer;The Classification Society, vol. 39(1), pages 191-216, March.
    • Handle: RePEc:spr:jclass:v:39:y:2022:i:1:d:10.1007_s00357-021-09403-7
    DOI: 10.1007/s00357-021-09403-7
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    References listed on IDEAS

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