# Lesson Archives

1. ## Determining The Optimal Number Of Clusters: 3 Must Know Methods

In this article, we'll describe different methods for determining the optimal number of clusters for k-means, k-medoids (PAM) and hierarchical clustering.
2. ## Assessing Clustering Tendency

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.
3. ## Heatmap in R: Static and Interactive Visualization

A heatmap is another way to visualize hierarchical clustering. It's also called a false colored image, where data values are transformed to color scale. Here, we'll demonstrate how to draw and arrange a heatmap in R.
4. ## Examples of Dendrograms Visualization

This article provides examples of beautiful dendrograms visualization using R software. Additionally, we show how to save and to zoom a large dendrogram.
5. ## Comparing Cluster Dendrograms in R

This article describes how to compare cluster dendrograms in R using the dendextend R package
6. ## Divisive Hierarchical Clustering

This article introduces the divisive clustering algorithms and provides practical examples showing how to compute divise clustering using R.
7. ## 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.
8. ## 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.
9. ## 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.
10. ## 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