Efficient robust methods via monitoring for clustering and multivariate data analysis. (April 2019)

• Record Type: Journal Article
• Authors: Riani, Marco; Atkinson, Anthony C.; Cerioli, Andrea; Corbellini, Aldo
• Is Part Of: Pattern recognition. Volume 88(2019:Apr.)
• Page Start: 246
• Page End: 260
• Publication Date: 2019-04
• Subjects: Bovine phlegmon -- "Car-bike" plot -- Clustering -- Eigenvalue constraint -- Forward search -- MCD -- MM-Estimation -- Modified BIC -- Outliers; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.11.016
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A new data-dependent method for determining the trimming level in robust clustering. We monitor the cluster structure as the trimming level changes. The robust clustering constrains the ratio of eigenvalues of dispersion matrices. The "car-bike" plot exhibits cluster stability as the eigenvalue constraint varies. Sizable bibliography relating the work to that in the machine learning and pattern recognition fields. Abstract: Monitoring the properties of single sample robust analyses of multivariate data as a function of breakdown point or efficiency leads to the adaptive choice of the best values of these parameters, eliminating arbitrary decisions about their values and so increasing the quality of estimators. Monitoring the trimming proportion in robust cluster analysis likewise leads to improved estimators. We illustrate these procedures on a sample of 424 cows with bovine phlegmon. For clustering we use a method which includes constraints on the eigenvalues of the dispersion matrices, so avoiding thread shaped clusters. The "car-bike" plot reveals the stability of clustering as the trimming level changes. The pattern of clusters and outliers alters appreciably for low levels of trimming.