Articles - Unsupervised machine learning

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 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]