Introduction to Unsupervised Learning

Unsupervised learning is a type of machine learning where an algorithm learns patterns and structures from unlabeled data—that is, data without predefined categories, labels, or outcomes. Unlike supervised learning, where the model is trained with input–output pairs, in unsupervised learning the system must discover hidden structures in the data on its own.

Key Characteristics:

• No labeled output: The algorithm only has input features, not target values.

• Pattern discovery: It groups, organizes, or reduces data based on similarities, structures, or relationships.

• Exploratory: Often used for data exploration, dimensionality reduction, or feature extraction.

Common Techniques in Unsupervised Learning:

1. Clustering

   • Groups data points into clusters based on similarity.

   • Example algorithms:

     • K-Means

     • Hierarchical Clustering

     • DBSCAN

   • Applications: customer segmentation, anomaly detection.

2. Dimensionality Reduction

   • Simplifies datasets by reducing the number of features while preserving essential information.

   • Example algorithms:

     • Principal Component Analysis (PCA)

     • t-SNE

     • Autoencoders

   • Applications: data visualization, noise reduction, preprocessing for supervised learning.

3. Association Rule Learning

   • Finds relationships between variables in large datasets.

   • Example: Market Basket Analysis (discovering items frequently bought together).

   • Algorithm: Apriori, FP-Growth.

Applications of Unsupervised Learning:

• Customer segmentation in marketing

• Recommendation systems (grouping similar users/items)

• Fraud detection and anomaly detection

• Data compression and noise reduction

• Organizing large-scale data (e.g., search engines, genomics)

👉 In short, unsupervised learning helps uncover hidden structures in data without prior labels, making it especially powerful for exploring unknown patterns and preparing data for further analysis.