# Lesson Archives

1. ## Fuzzy Clustering Essentials

Fuzzy clustering is also known as soft method. Standard clustering (K-means, PAM) approaches produce partitions, in which each observation belongs to only one cluster. This is known as hard clustering. In Fuzzy clustering, items can be a member of more than one cluster. Each item has a set of membership coefficients corresponding to the degree of being in a given cluster. In this article, we’ll describe how to compute fuzzy clustering using the R software.
2. ## Hierarchical K-Means Clustering: Optimize Clusters

The hierarchical k-means clustering is an hybrid approach for improving k-means results. In this article, you will learn how to compute hierarchical k-means clustering in R
3. ## Computing P-value for Hierarchical Clustering

This article describes the R package pvclust, which uses bootstrap resampling techniques to compute p-value for each hierarchical clusters.
4. ## Choosing the Best Clustering Algorithms

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.
5. ## Cluster Validation Statistics: Must Know Methods

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.
6. ## 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.
7. ## 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.
8. ## 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.
9. ## 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.
10. ## Comparing Cluster Dendrograms in R

This article describes how to compare cluster dendrograms in R using the dendextend R package