Cluster Analysis
1. Introduction
Cluster Analysis is an unsupervised learning and multivariate statistical technique used to group a
set of observations into clusters such that objects within the same cluster are more similar to each
other than to objects in different clusters. Unlike classification methods, cluster analysis does not
rely on predefined labels; instead, it discovers structure directly from the data.
The primary objective of cluster analysis is to identify natural groupings in data. These groupings
may represent hidden patterns, subpopulations, or structures that are not immediately apparent.
Cluster analysis is widely used in data mining, biology, marketing, social sciences, image
processing, and machine learning.
Clustering is particularly useful for:
• Exploratory data analysis
• Pattern recognition
• Market segmentation
• Anomaly detection
• Data summarization
Because clustering results depend strongly on the choice of similarity measure and algorithm,
careful methodological decisions are essential for meaningful outcomes.
2. Similarity Measures
Similarity measures quantify how alike two observations are. The choice of similarity or distance
measure directly influences the clustering result.
Distance-Based Measures
Euclidean Distance
• The most commonly used distance measure
• Measures straight-line distance between two points
