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

Clustering Algorithms

• K-Means: Partitioning into k clusters
• Hierarchical: Dendrograms showing nested clusters