Hennig, C;
Viroli, C;
Anderlucci, L;
(2019)
Quantile-based clustering.
Electronic Journal of Statistics
, 13
(2)
pp. 4849-4883.
10.1214/19-ejs1640.
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Abstract
A new cluster analysis method, K-quantiles clustering, is introduced. K-quantiles clustering can be computed by a simple greedy algorithm in the style of the classical Lloyd’s algorithm for K-means. It can be applied to large and high-dimensional datasets. It allows for within-cluster skewness and internal variable scaling based on within-cluster variation. Different versions allow for different levels of parsimony and computational efficiency. Although K-quantiles clustering is conceived as nonparametric, it can be connected to a fixed partition model of generalized asymmetric Laplace-distributions. The consistency of K-quantiles clustering is proved, and it is shown that K-quantiles clusters correspond to well separated mixture components in a nonparametric mixture. In a simulation, K-quantiles clustering is compared with a number of popular clustering methods with good results. A high-dimensional microarray dataset is clustered by K-quantiles.
| Type: | Article |
|---|---|
| Title: | Quantile-based clustering |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1214/19-ejs1640 |
| Publisher version: | https://doi.org/10.1214/19-ejs1640 |
| Language: | English |
| Additional information: | © The Authors 2019. Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Fixed partition model, quantile discrepancy, high dimensional clustering, nonparametric mixture |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10095426 |
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