Explanation
Imagine sorting a box of random objects without any instructions. You might group them by colour, size, or shape, discovering patterns on your own. Unsupervised learning is similar: it's a type of machine learning where the algorithm explores data without any pre-defined labels or guidance. It's like giving the computer a blank canvas and asking it to find its own way. The algorithm identifies hidden structures, clusters similar data points, and uncovers underlying relationships. Instead of predicting a specific outcome, unsupervised learning aims to understand the inherent organisation of the data. This can be useful for tasks such as customer segmentation, anomaly detection, and dimensionality reduction. It is a powerful way of finding the unknown unknowns.
Examples
Consumer Example
Think about online shopping. Recommendation engines use unsupervised learning to group customers with similar purchasing habits. Based on these groupings, the system can suggest products that a particular customer might be interested in. It's like having a personal shopping assistant that anticipates your needs based on your past behaviour and the behaviour of others like you.
Business Example
Imagine a bank wanting to identify fraudulent transactions. Unsupervised learning can analyse transaction data to identify unusual patterns that deviate from the norm. These anomalies might indicate fraudulent activity, allowing the bank to investigate further and prevent financial losses. It's like having a sophisticated fraud detection system that learns to recognise suspicious behaviour in real time.