---
title: "Clustering in R: Methods, Algorithms, and Examples"
url: https://daily.dev/posts/clustering-in-r-methods-algorithms-and-examples-xmy5h3bfd
source_url: https://domino.ai/blog/clustering-in-r
type: article
source: "Domino Data Lab"
published: 2026-06-16T15:43:43.078Z
updated: 2026-06-16T15:44:05.805Z
tags: ["machine-learning", "data-science", "r"]
reading_time: 24
upvotes: 0
comments: 0
language: en
---

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# Clustering in R: Methods, Algorithms, and Examples

**[Domino Data Lab](https://daily.dev/sources/ddl)** · 24 min read · 0 upvotes · 0 comments

## Summary

A practical tutorial on clustering in R covering three main algorithms: k-means, Partitioning Around Medoids (PAM), and hierarchical clustering. Includes working R code using the UCI wine dataset and World Bank country data. Covers how to choose the number of clusters using Hartigan's Rule, the Gap statistic, elbow method, and silhouette scores. Also discusses linkage methods for hierarchical clustering, visualization techniques including dendrograms and world maps, and package recommendations for scaling (fastcluster, ClusterR, tidyclust) and reproducibility (renv).

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://domino.ai/blog/clustering-in-r>

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#data-science](https://daily.dev/tags/data-science), [#r](https://daily.dev/tags/r)

[View this post on daily.dev](https://daily.dev/posts/clustering-in-r-methods-algorithms-and-examples-xmy5h3bfd)