We introduce a robust clustering procedure for parsimonious model-based clustering. The classical mclust framework is robustified through impartial trimming and eigenvalue-ratio constraints (the tclust framework, which is robust but not affine invariant). An advantage of our resulting mtclust approach is that eigenvalue-ratio constraints are not needed for certain model formulations, leading to affine invariant robust parsimonious clustering. We illustrate the approach via simulations and a benchmark real data example. R code for the proposed method is available at https://github.com/afarcome/mtclust.
Dotto, F., Farcomeni, A. (2019). Robust inference for parsimonious model-based clustering. JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 89(3), 414-442 [10.1080/00949655.2018.1554659].
Robust inference for parsimonious model-based clustering
Farcomeni, Alessio
2019-01-01
Abstract
We introduce a robust clustering procedure for parsimonious model-based clustering. The classical mclust framework is robustified through impartial trimming and eigenvalue-ratio constraints (the tclust framework, which is robust but not affine invariant). An advantage of our resulting mtclust approach is that eigenvalue-ratio constraints are not needed for certain model formulations, leading to affine invariant robust parsimonious clustering. We illustrate the approach via simulations and a benchmark real data example. R code for the proposed method is available at https://github.com/afarcome/mtclust.File | Dimensione | Formato | |
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