This article describes the R package pvclust, which uses bootstrap resampling techniques to compute p-value for each hierarchical clusters. In this article, we’ll start by describing the different measures in the clValid R package for comparing clustering algorithms. Next, we’ll present the function clValid(). Finally, we’ll provide R scripts for validating clustering results and comparing clustering algorithms. In this article, we start by describing the different methods for clustering validation. Next, we'll demonstrate how to compare the quality of clustering results obtained with different clustering algorithms. Finally, we'll provide R scripts for validating clustering results. 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.