Deviations from theoretical assumptions together with the presence of certain amount of
outlying observations are common in many practical statistical applications. This is also the …

The monograph Data Clustering: Theory, Algorithms, and Applications was published in
2007. Starting with the common ground and knowledge for data clustering, the monograph …

This paper synthesizes the results, methodology, and research conducted concerning the K‐
means clustering method over the last fifty years. The K‐means method is first introduced …

We introduce a new method for performing clustering with the aim of fitting clusters with
different scatters and weights. It is designed by allowing to handle a proportion α of …

Functional data sets appear in many areas of science. Although each data point may be
seen as a large finite-dimensional vector it is preferable to think of them as functions, and …

Because high-breakdown estimators (HBEs) are impractical to compute exactly in large
samples, approximate algorithms are used. The algorithm generally produces an estimator …

A robust cluster procedure for functional data is introduced. It is based on the notion of
impartial trimming. Existence and consistency results are obtained. Furthermore, a feasible …

Fuzzy clustering for interval-valued data helps us to find natural vague boundaries in such
data. The Fuzzy c-Medoids Clustering (FcMdC) method is one of the most popular clustering …

Exploratory graphical tools based on trimming are proposed for detecting main clusters in a
given dataset. The trimming is obtained by resorting to trimmed k-means methodology. The …

In one dimension, order statistics and ranks are widely used because they form a basis for
distribution free tests and some robust estimation procedures. In more than one dimension …