# Lesson Archives

1. ## Divisive Hierarchical Clustering

This article introduces the divisive clustering algorithms and provides practical examples showing how to compute divise clustering using R.
2. ## Agglomerative Hierarchical Clustering

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 in R : Clustering Large Applications

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-Medoids in R: Algorithm and Practical Examples

The k-medoids (or PAM) algorithm is a non-parametric alternative of k-means clustering for partitioning a dataset. This article describes the PAM algorithm and shows how to compute PAM in R software.
5. ## K-Means Clustering in R: Algorithm and Practical Examples

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
6. ## Cluster Analysis Example: Quick Start R Code

This chapter describes a cluster analysis example using R software. We provide a quick start R code to compute and visualize K-means and hierarchical clustering.
7. ## Clustering Distance Measures

In this article, we describe the common distance measures used to compute distance matrix for cluster analysis. We also provide R codes for computing and visualizing distances.
8. ## Data Preparation and R Packages for Cluster Analysis

This chapter introduces how to prepare your data for cluster analysis and describes the essential R package for cluster analysis.
9. ## 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.
10. ## Compute and Add new Variables to a Data Frame in R

This tutorial describes how to compute and add new variables to a data frame in R.