Learning Without Labels
Unsupervised learning deals with unlabeled data. There are no predefined correct answers. Instead, the algorithm tries to find hidden structures, patterns, or groupings in the data on its own. It's like being given a box of mixed puzzle pieces and figuring out how they fit together without seeing the box picture.
Types of Unsupervised Learning
┌──────────────────────────────────────────────────────┐
│ UNSUPERVISED LEARNING │
│ │
│ ┌────────────────┐ ┌───────────────┐ ┌────────┐ │
│ │ CLUSTERING │ │ DIMENSIONALITY│ │ANOMALY │ │
│ │ │ │ REDUCTION │ │DETECT. │ │
│ │ Group similar │ │ │ │ │ │
│ │ data points │ │ Simplify │ │Find │ │
│ │ together │ │ complex data │ │unusual │ │
│ │ │ │ into fewer │ │data │ │
│ │ Examples: │ │ dimensions │ │points │ │
│ │ • Customer │ │ │ │ │ │
│ │ segments │ │ Examples: │ │Examples:│ │
│ │ • Document │ │ • PCA │ │• Fraud │ │
│ │ topics │ │ • t-SNE │ │• Outli-│ │
│ │ • Gene │ │ • UMAP │ │ ers │ │
│ │ expression │ │ │ │ │ │
│ └────────────────┘ └───────────────┘ └────────┘ │
└──────────────────────────────────────────────────────┘
Clustering
Clustering groups similar data points together. The most famous algorithm is K-Means, which partitions data into K clusters based on distance. Other algorithms include DBSCAN (density-based), Hierarchical clustering, and Gaussian Mixture Models.
K-Means Clustering Example:
Data Points: After Clustering:
. . * * Cluster 1: . . .
. . * Cluster 2: * * *
. *
. . * Each point assigned
to nearest centroid
Dimensionality Reduction
When your data has too many features (dimensions), it becomes hard to work with. Dimensionality reduction simplifies the data while keeping the important information. PCA (Principal Component Analysis) is the most common technique — it finds the axes that capture the most variance in the data.
Anomaly Detection
Anomaly detection finds data points that don't fit the normal pattern. This is useful for fraud detection, network security, and manufacturing quality control. If most transactions are between $10 and $200, a $50,000 transaction is an anomaly worth investigating.
When to Use Unsupervised Learning
Use unsupervised learning when you don't have labeled data, or when you want to explore your data to discover hidden patterns. Customer segmentation, topic modeling, data visualization, and pre-processing for supervised learning are all great use cases.