Clustering is one of the most widely used unsupervised learning techniques. However, it is
well‐known that outliers can have a significantly adverse impact on commonly applied …

The purpose of this paper is to show in regression clustering how to choose the most
relevant solutions, analyze their stability, and provide information about best combinations of …

Four different approaches to robust fuzzy clustering of time series are presented and
compared with respect to other existent approaches. These approaches are useful to cluster …

We propose a model-based clustering procedure where each component can take into
account cluster-specific mild outliers through a flexible distributional assumption, and a …

When researchers publish new cluster algorithms, they usually demonstrate the strengths of
their novel approaches by comparing the algorithms' performance with existing competitors …

A new methodology for constrained parsimonious model-based clustering is introduced,
where some tuning parameter allows to control the strength of these constraints. The …

In a standard classification framework a set of trustworthy learning data are employed to
build a decision rule, with the final aim of classifying unlabelled units belonging to the test …

Three important issues are often encountered in Supervised and Semi-Supervised
Classification: class memberships are unreliable for some training units (label noise), a …

We assess the performance of state-of-the-art robust clustering tools for regression
structures under a variety of different data configurations. We focus on two methodologies …

Many clustering algorithms when the data are curves or functions have been recently
proposed. However, the presence of contamination in the sample of curves can influence …