Publication
Title
Regularized K-means through hard-thresholding
Author
Abstract
We study a framework for performing regularized K-means, based on direct penalization of the size of the cluster centers. Different penalization strategies are considered and compared in a theoretical analysis and an extensive Monte Carlo simulation study. Based on the results, we propose a new method called hard-threshold K-means (HTK-means), which uses an `0 penalty to induce sparsity. HTK-means is a fast and competitive sparse clustering method which is easily interpretable, as is illustrated on several real data examples. In this context, new graphical displays are presented and used to gain further insight into the data sets.
Language
English
Source (journal)
Journal of machine learning research
Publication
2022
Volume/pages
23 (2022) , p. 1-48
Full text (open access)
UAntwerpen
Research group
Publication type
Subject
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Creation 27.02.2024
Last edited 28.02.2024
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