ATTENTION: This is a web archive! The IMS Group was split up in 2018 and does not exist anymore. Recent work of former members can be found at the VR/AR Group and the Computer Vision Group.

Interactive Media Systems, TU Wien

Robust k-means-based clustering for high-dimensional data

By Peter Filzmoser, Sarka Brodinova, Thomas Ortner, Christian Breiteneder, and Maia Rohm


We introduce a robust k-means-based clustering method for high-dimensional data where not only outliers but also a large number of noise variables are very likely to be present. Although Kondo et al. [2] already addressed such an application scenario, our approach goes even further. Firstly, the introduced method is designed to identify clusters, informative variables, and outliers simultaneously. Secondly, the proposed clustering technique additionally aims at optimizing required parameters, e.g. the number of clusters. This is a great advantage over most existing methods. Moreover, the robustness aspect is achieved through a robust initialization [3] and a proposed weighting function using the Local Outlier Factor [1]. The weighting function provides a valuable source of information about the outlyingness of each observation for a subsequent outlier detection. In order to reveal both clusters and informative variables properly, the approach uses a lasso-type penalty [4]. The method has thoroughly been tested on simulated as well as on real highdimensional datasets. The conducted experiments demonstrated a great ability of the clustering method to identify clusters, outliers, and informative variables.


P. Filzmoser, S. Brodinova, T. Ortner, C. Breiteneder, M. Rohm: "Robust k-means-based clustering for high-dimensional data"; Talk: International Conference on Robust Statistics (ICORS 2019), Guayaquil (invited); 05-28-2019 - 05-31-2019.


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