How to Perform a Cluster Analysis in R

Written by Coursera Staff • Updated on

Building skills in data analysis techniques such as cluster analyses can help you analyze and interpret information more effectively. Learn what a cluster analysis is and how to perform your own.

[Featured image] A marketer uses cluster analysis in R to identify specific groups of customers.

Cluster analyses are a great tool for taking structured or unstructured data and grouping information with similar features. R, a popular statistical programming language, allows you to perform cluster analysis with your data and visualize results in easily interpretable and shareable ways. This article explores different types of cluster analyses, how to start learning R, and the basic steps to perform a cluster analysis with this software.

What is cluster analysis?

Cluster analysis is a powerful tool in data science used to group data or objects so that each group (cluster) of objects is more closely related in value to each other than other groups. This technique is popular with professions in various fields, including marketing, biology, and social sciences, to uncover patterns and relationships in data.

You can perform cluster analysis with statistical programming languages such as SAS and R. One benefit of R is that it’s a free, open-source programming environment specifically designed for statistical computing and graphics. Using R software can make cluster analysis more straightforward thanks to a comprehensive set of packages and functions. These tools simplify the process of clustering and interpreting complex data sets.

Types of cluster analysis in R

When performing a cluster analysis, you can use a few different methods in R. Three of the most popular methods are as follows.

1. Hierarchical clustering 

This method builds a hierarchy of clusters by starting with individual points and combining them into larger clusters (agglomerative) or by starting with the entire data set and dividing it into smaller clusters (divisive). Agglomerative clustering is typically a good choice if you want to identify small clusters, while divisive clustering is better if you are looking for large clusters. The clusters this method represents after the clustering process are defined by the centroid or the medoid. This type of method is reproducible, which you may want to consider depending on your purpose.

2. K-means clustering 

This is a popular method used for clustering. When using this approach, you will specify the number of clusters you want, which is your “k” value. The algorithm then works to classify objects that are most similar into groups. The objects are grouped based on their distance to a cluster's nearest mean (centroid). The process iteratively refines the groupings to minimize variances within each cluster. One limitation of this approach is that it is sensitive to outliers, so it’s important you understand the structure of your data before deciding on the approach.

3. Density-based spatial clustering of applications with noise (DBSCAN) 

DBSCAN looks at how data are grouped, marking certain ranges of values as high-density regions and labeling those in low-density regions as outliers or noise. This helps to see where values of data cluster together. With DBSCAN, you don’t need to specify the number of clusters. However, choosing appropriate values for neighbors and minimum features will influence your results.

Getting started with R

If you decide to use R for your cluster analysis, your first step is to install R, set up the environment, and learn a few basic commands. 

  • Installing R: First, you need to install R from the Comprehensive R Archive Network (CRAN). You can also install RStudio, a popular integrated development environment for R.

  • Setting up: Once installed, you can set up your environment by installing packages like cluster, factoextra, and dendextend, which are commonly used for clustering and data visualization.

  • Learning basic commands: Next, familiarize yourself with basic R commands and syntax. For cluster analysis, understanding data import (read.csv, read.table), data manipulation (such as handling missing values), and basic statistical functions may benefit you.

How to perform a cluster analysis in R

While individual steps may vary depending on your analysis needs, following these basic steps is a good starting point. Consider the following method to prepare your data and perform a basic cluster analysis. 

1. Prepare your data.

Before diving into cluster analysis in R, preparing your data correctly is an important step for meaningful results. Start by cleaning your data. This involves dealing with missing values, organizing your columns and rows, and correcting errors such as duplicates. In R, functions like na.omit() can help remove missing values, and unique() can identify duplicates.

2. Normalize your data.

Clustering algorithms respond to how you scale your data. With data normalization, each feature contributes equally to the results. You can use functions like scale() in R to normalize your data and improve your results.

3. Choose your variables.

Once you clean and normalize your data, you’ll choose relevant variables for clustering. You can base this selection on domain knowledge to avoid irrelevant variables or use statistical methods like principal component analysis (PCA) to find the most weighted variables.

4. Decide on your cluster method.

For hierarchical clustering, use functions like hclust(). You can use the dist() function first to compute the distance matrix.

For k-means clustering, you may use the factoextra package or the kmeans() function. It requires specifying the number of clusters.

For DBSCAN, you can use the dbscan package in R for basic DBSCAN functions. It’s good for data containing clusters of similar density.

5. Visualize your clusters.

Visualization is a key step in interpreting the results of cluster analysis. You can use images such as scatter plots, dendrograms, pie charts, bar plots, and pair plots to visualize clusters. To make these visualizations, you can use a visualization package in R called ggplot2 to create sophisticated images customized to your needs.

With ggplot2, you can enhance scatter plots with color to represent clusters and use faceting to display multiple dimensions of data. Visualizations help assess the clustering tendency of your data, understand the shape and size of clusters, and identify any outliers or anomalies.

Learn more on Coursera.

You can continue to learn about data analysis techniques in R with popular R tutorials and courses on the Coursera learning platform. To begin, consider taking the Data Analysis with R course offered by IBM. This course will guide you through data preparation, model comparison, coding techniques, and more at a self-guided pace. Upon completion, gain a shareable Professional Certificate to include in your resume, CV, or LinkedIn profile.

Keep reading

Updated on
Written by:

Editorial Team

Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.