Talks and Poster Presentations (with Proceedings-Entry):
S. Brodinova, P. Filzmoser, T. Ortner, M. Zaharieva, C. Breiteneder:
"Robust and sparse clustering for high-dimensional data";
Talk: Conference of the CLAssification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS),
Milan, Italy;
2017-09-13
- 2017-09-15; in: "CLADAG 2017 Book of Short Papers",
(2017),
ISBN: 978-88-99459-71-0.
English abstract:
We introduce a robust and sparse clustering procedure for high-dimensional data. The robustness aspect is addressed by a weighting function incorporated in the k-means procedure, consequently leading to an automatic weight assignment for each observation. The sparsity aspect is given by a lasso-type penalty on weighted between-cluster sum of squares. We additionally propose a framework for determining the optimal number of both clusters and variables that contribute to a cluster separation.
German abstract:
We introduce a robust and sparse clustering procedure for high-dimensional data. The robustness aspect is addressed by a weighting function incorporated in the k-means procedure, consequently leading to an automatic weight assignment for each observation. The sparsity aspect is given by a lasso-type penalty on weighted between-cluster sum of squares. We additionally propose a framework for determining the optimal number of both clusters and variables that contribute to a cluster separation.
Keywords:
k-means clustering, outlier detection, high-dimensional data, variable selection, parameter selection
Related Projects:
Project Head Maia Zaharieva:
Unusual sequences detection in very large video collections
Created from the Publication Database of the Vienna University of Technology.