Lesson Archives

  1. In this chapter, we start by describing why we should evaluate the clustering tendency before applying any clustering method on a data. Next, we provide statistical and visual methods for assessing the clustering tendency in R software.
  2. In this article, we start by describing the agglomerative clustering algorithms. Next, we provide R lab sections with many examples for computing and visualizing hierarchical clustering. We continue by explaining how to interpret dendrogram. Finally, we provide R codes for cutting dendrograms into groups.
  3. CLARA is a clustering technique that extends the k-medoids (PAM) methods to deal with data containing a large number of objects in order to reduce computing time and RAM storage problem. In this article, you will learn: 1) the basic steps of CLARA algorithm; 2) Examples of computing CLARA in R software using practical examples.
  4. K-means clustering is one of the most commonly used unsupervised machine learning algorithm for partitioning a given data set into a set of k groups. In this tutorial, you will learn: 1) the basic steps of k-means algorithm; 2) How to compute k-means in R software using practical examples; and 3) Advantages and disavantages of k-means clustering