Robust and sparse \(k\)-means clustering for high-dimensional data
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Publication:2303055
DOI10.1007/s11634-019-00356-9zbMath1459.62106arXiv1709.10012OpenAlexW2964346923MaRDI QIDQ2303055
Maia Rohm, Thomas Ortner, Peter Filzmoser, Christian Breiteneder, Sarka Brodinova
Publication date: 2 March 2020
Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1709.10012
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Robustness and adaptive procedures (parametric inference) (62F35)
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Cites Work
- Unnamed Item
- Exploring the number of groups in robust model-based clustering
- Estimating the Number of Clusters in a Data Set Via the Gap Statistic
- Robust fitting of mixtures using the trimmed likelihood estimator
- Snipping for robust \(k\)-means clustering under component-wise contamination
- A general trimming approach to robust cluster analysis
- Outlier identification in high dimensions
- Trimmed \(k\)-means: An attempt to robustify quantizers
- A reweighting approach to robust clustering
- Modelling the role of variables in model-based cluster analysis
- Robustness properties of \(S\)-estimators of multivariate location and shape in high dimension
- The power of monitoring: how to make the most of a contaminated multivariate sample
- Trimming algorithms for clustering contaminated grouped data and their robustness
- A review of robust clustering methods
- Outlier Analysis
- Finding the Number of Clusters in a Dataset
- Robustness Properties of k Means and Trimmed k Means
- Cluster detection and clustering with random start forward searches
- A Framework for Feature Selection in Clustering
- Variable Selection for Model-Based Clustering
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