In this article, we'll describe different methods for determining the optimal number of clusters for k-means, k-medoids (PAM) and hierarchical clustering. 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. 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. This article provides examples of beautiful dendrograms visualization using R software. Additionally, we show how to save and to zoom a large dendrogram. This article describes how to compare cluster dendrograms in R using the dendextend R package This article introduces the divisive clustering algorithms and provides practical examples showing how to compute divise clustering using R. 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. 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. 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. 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