What is R Cluster Analysis?

Clustering is a type of unsupervised learning. It’s used when we don’t have labeled data and need to discover patterns or groupings.

Definition: Clustering is the process of grouping similar data points together.

So, clustering in R means grouping similar objects into one cluster and separating dissimilar objects into different clusters.

Goal of Clustering in R

The main goal of clustering is to find hidden patterns or natural groupings in your data without knowing the labels in advance.

But remember, there is no single best method for clustering – it all depends on your data and your final objective.

Types of Clustering in R

1️ Hard Clustering

Each data point belongs to only one cluster.

2️ Soft Clustering

Each data point is assigned a probability of belonging to different clusters.

What a Good Clustering Algorithm Should Do:

Work on large datasets (Scalability)

Handle different data types

Find clusters of any shape

Handle noise & outliers

Work in high dimensions

Provide results that are easy to interpret

Real-World Applications of R Clustering

Problems with R Clustering

Some clustering algorithms are slow for big data.

Results can vary depending on how you interpret them.

It’s hard to meet all clustering requirements with one algorithm.

Types of Clustering Algorithms in R

i. Distribution Models

Assumes data fits a probability distribution (like Gaussian)

Example: Model-based clustering using EM (Expectation-Maximization)

ii. Connectivity Models

Clusters are formed based on distance between data points

Example: Hierarchical Clustering

iii. Density Models

Clusters formed in dense areas of the data

Example: DBSCAN (Density-Based Spatial Clustering)

iv. Centroid Models

Based on the center (centroid) of clusters

Example: K-Means Clustering

Popular Clustering Algorithms in R

a. K-Means Clustering

K-Means is the most common and fast clustering method.
It divides the data into K groups based on similarity.

Steps:

Choose the number of clusters (K)

Pick K random points as cluster centers

Assign each point to the nearest center

Recalculate the centers

Repeat until clusters don’t change

Best for: well-separated and spherical clusters

b. DBSCAN Clustering (Density-Based)

DBSCAN groups data points in high-density areas.
Great for irregular shapes and noisy datasets.

Parameters:

eps: Distance radius to define neighborhood

minPts: Minimum points required to form a dense region

Types of Points:

Core: Points with at least minPts neighbors

Border: Near core points but with fewer neighbors

Outliers: Not part of any cluster

Pros:

No need to specify number of clusters

Handles outliers well

Can find clusters of any shape

Cons:

Sensitive to eps and minPts

Can struggle when data density varies

c. Hierarchical Clustering

Builds a tree of clusters (dendrogram) by:

Starting with each point as its own cluster

Merging the closest pairs step-by-step

Pros:

No need to set the number of clusters

Easy to understand visual hierarchy

Cons:

Slow for large datasets

R Packages for Clustering

dbscan – for DBSCAN algorithm

cluster – for K-means and hierarchical clustering

factoextra – for visualizing clusters

fpc, mclust, clustertend – advanced clustering tools

Conclusion

Clustering in R is a powerful technique to analyze unlabeled data and find hidden groupings. With the right choice of algorithm, you can make sense of customer data, biological samples, geographic patterns, and more.