Lesson Archives

  1. 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.
  2. 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
  3. Compute Summary Statistics in R

    This tutorial introduces how to easily compute statistcal summaries in R using the dplyr package. You will learn, how to compute summary statistics for ungrouped data, as well as, for data that are grouped by one or multiple variables.