A reweighting approach to robust clustering

An iteratively reweighted approach for robust clustering is presented in this work. The method is initialized with a very robust clustering partition based on an high trimming level. The initial partition is then refined to reduce the number of wrongly discarded observations and substantially increa...

Full description

Saved in:
Bibliographic Details
Published in:Statistics and computing 2018-03, Vol.28 (2), p.477-493
Main Authors: Dotto, Francesco, Farcomeni, Alessio, García-Escudero, Luis Angel, Mayo-Iscar, Agustín
Format: Article
Language:English
Subjects:
Artificial Intelligence
Clustering
Mathematics and Statistics
Partitions
Probability and Statistics in Computer Science
Robustness (mathematics)
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:An iteratively reweighted approach for robust clustering is presented in this work. The method is initialized with a very robust clustering partition based on an high trimming level. The initial partition is then refined to reduce the number of wrongly discarded observations and substantially increase efficiency. Simulation studies and real data examples indicate that the final clustering solution has both good properties in terms of robustness and efficiency and naturally adapts to the true underlying contamination level.
ISSN:0960-3174
1573-1375
DOI:10.1007/s11222-017-9742-x