Unsupervised Learning
Machine learning where models find patterns in data without labelled examples. The algorithm discovers hidden structures, clusters, or relationships autonomously.
In-Depth Explanation
Unsupervised learning discovers patterns in data without explicit labels or correct answers. The model finds structure autonomously, making it valuable for exploration and discovery.
Key unsupervised learning approaches:
- Clustering: Grouping similar items (K-means, hierarchical, DBSCAN)
- Dimensionality reduction: Compressing data while preserving structure (PCA, t-SNE, UMAP)
- Anomaly detection: Finding unusual patterns or outliers
- Association rules: Discovering relationships between variables
When unsupervised learning excels:
- Exploring new datasets without predefined categories
- Customer segmentation without predetermined groups
- Detecting anomalies when you don't know what anomalies look like
- Data preprocessing and feature learning
Challenges:
- No ground truth to measure accuracy directly
- Results require interpretation
- May find patterns that aren't meaningful
- Harder to validate than supervised approaches
Business Context
Unsupervised learning is powerful for customer segmentation, anomaly detection, and exploratory analysis where you don't know what patterns exist yet.
How Clever Ops Uses This
We use unsupervised learning to help Australian businesses discover customer segments, detect fraud patterns, and uncover insights in unstructured data.
Example Use Case
"Automatically segmenting customers into distinct groups based on behaviour patterns, without predefined segment definitions."