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arXiv · 1709.10012

Robust and sparse k-means clustering for high-dimensional data

Sarka Brodinova·Peter Filzmoser·Thomas Ortner·Christian Breiteneder·Maia Zaharieva
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Abstract

In real-world application scenarios, the identification of groups poses a significant challenge due to possibly occurring outliers and existing noise variables. Therefore, there is a need for a clustering method which is capable of revealing the group structure in data containing both outliers and noise variables without any pre-knowledge. In this paper, we propose a $k$-means-based algorithm incorporating a weighting function which leads to an automatic weight assignment for each observation. In order to cope with noise variables, a lasso-type penalty is used in an objective function adjusted by observation weights. We finally introduce a framework for selecting both the number of clusters and variables based on a modified gap statistic. The conducted experiments on simulated and real-world data demonstrate the advantage of the method to identify groups, outliers, and informative variables simultaneously.

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Sarka Brodinova, Peter Filzmoser, Thomas Ortner, Christian Breiteneder, Maia Zaharieva. 2017-09-28. Robust and sparse k-means clustering for high-dimensional data. https://arxiv.org/abs/1709.10012

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