Articles - Multivariate Analysis

As described in previous chapters, a dendrogram is a tree-based representation of a data created using hierarchical clustering methods (Chapter @ref(agglomerative-clustering)). In this article,... [Read more]

After showing how to compute hierarchical clustering (Chapter @ref(agglomerative-clustering)), we describe, here, how to compare two dendrograms using the dendextend R package. The... [Read more]

The agglomerative clustering is the most common type of hierarchical clustering used to group objects in clusters based on their similarity. It’s also known as AGNES (Agglomerative Nesting). The... [Read more]

The k-medoids algorithm is a clustering approach related to k-means clustering (chapter @ref(kmeans-clustering)) for partitioning a data set into k groups or clusters. In k-medoids clustering,... [Read more]

K-means clustering (MacQueen 1967) is the most commonly used unsupervised machine learning algorithm for partitioning a given data set into a set of k groups (i.e. k clusters), where k represents... [Read more]

The classification of observations into groups requires some methods for computing the distance or the (dis)similarity between each pair of observations. The result of this computation is known... [Read more]

This page presents series of course videos on clustering methods for analyzing multivariate data. The main objective is either (i) to identify groups of individuals with a similar profile,... [Read more]

In this article, you’ll learn how MFA (Multiple Factor Analysis) works, as well as, how to easily compute and interpret MFA in R using the FactoMineR package. Recall that MFA is a multivariate... [Read more]

Factor analysis of mixed data (FAMD) is dedicated to analyze a data set containing both categorical and continuous variables. This article provides a quick start R code and video showing a... [Read more]

This article presents quick start R code and video series for computing MCA (Multiple Correspondence Analysis) in R, using the FactoMineR package. Recall that MCA is used for analyzing... [Read more]

This page shows quick start R code to compute correspondence analysis- CA in R using the FactoMineR package. Additionaly, we present series of course videos on correspondence analysis, which is a... [Read more]

This article starts by providing a quick start R code for computing PCA in R, using the FactoMineR, and continues by presenting series of PCA video courses (by François Husson). Recall that PCA... [Read more]