Robust clustering of high-dimensional data
Charles Bouveyron, Anastasios Bellas · HAL (Le Centre pour la Communication Scientifique Directe) · 2011
We address the problem of robust clustering of high - di- mensional data, which is recurrent in real-world applications. Existing robust clustering methods are unfortunately sensitive in high dimension, while existing approaches for high-dimensional data are in general not ro- bust. We propose a hybrid iterative EM-based algorithm that combines an efficient high-dimensional clustering algorithm and the trimming tech- nique. We test our algorithm on synthetic and real-world data from the domain of aircraft engine health monitoring and show its efficiency for high-dimensional noisy datasets.