As a generalization of simple correspondence analysis, multiple correspondence analysis
(MCA) is a powerful technique for handling larger, more complex datasets, including the …

A general method for exploring multivariate data by comparing different estimates of
multivariate scatter is presented. The method is based on the eigenvalue–eigenvector …

Principal component analysis (PCA) is an exploratory statistical method for graphical
description of the information present in large datasets. In most applications, PCA consists of …

Scatter matrices generalize the covariance matrix and are useful in many multivariate data
analysis methods, including well-known principal component analysis (PCA), which is …

In high reliability standards fields such as automotive, avionics or aerospace, the detection
of anomalies is crucial. An efficient methodology for automatically detecting multivariate …

We present GeoXp, an R package implementing interactive graphics for exploratory spatial
data analysis. We use a data set concerning public schools of the French Midi-Pyr? en? ees …

Huge clustering is often required in the field of DNA microarray (DeRisi et al., 1997)
analysis. A new use of clustering results appears with presentation and exploration software …

Outliers in maximum daily rainfall can play a considerable role in unreal analysis leading to
unreal predictions. Therefore, accurate statistical determination of data to find outliers is very …

Detecting outliers in the context of multivariate data is known as an important but difficult task
and there already exist several detection methods. Most of the proposed methods are based …

For multivariate data, tandem clustering is a well-known technique aiming to improve cluster
identification through initial dimension reduction. Nevertheless, the usual approach using …