Some issues in robust clustering
This addresses foundational methodological problems for researchers developing robust clustering algorithms, but is purely theoretical without empirical validation.
The paper identifies key theoretical issues in robust clustering using Gaussian mixture models, including ambiguous outlier definitions, interactions with cluster number estimation, and limitations of existing stability measurements.
Some key issues in robust clustering are discussed with focus on Gaussian mixture model based clustering, namely the formal definition of outliers, ambiguity between groups of outliers and clusters, the interaction between robust clustering and the estimation of the number of clusters, the essential dependence of (not only) robust clustering on tuning decisions, and shortcomings of existing measurements of cluster stability when it comes to outliers.