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Clustering Analysis

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Identify groups and patterns in data using k-means, hierarchical clustering, and DBSCAN for cluster discovery, customer segmentation, and unsupervised learning

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# Clustering Analysis

## Overview

Clustering partitions data into groups of similar observations without pre-defined labels, enabling discovery of natural patterns and structures in data.

## When to Use

- Segmenting customers based on purchasing behavior or demographics
- Discovering natural groupings in data without prior knowledge of categories
- Identifying market segments for targeted marketing campaigns
- Organizing large datasets into meaningful categories for further analysis
- Finding patterns in gene expression data or medical imaging
- Grouping documents, products, or users by similarity for recommendation systems
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