Artificial intelligence relies on different types of learning to process data and solve problems. Among them, unsupervised learning plays a key role in analyzing complex data without direct human intervention.
This article, written by the Yiaho team, explains what unsupervised learning is, gives a concrete example, compares this type of learning to supervised learning, and explores its typical uses and tasks.
What is unsupervised learning?
Unsupervised learning is a machine learning method where an algorithm analyzes data without labels or predefined instructions. Unlike other approaches, there are no “correct” data or expected results provided to the algorithm. The goal is to discover patterns, structures, or hidden relationships in the data.
For example, imagine a set of photos without any indication of what they contain. The unsupervised learning algorithm will examine these images and identify similarities or groups (such as grouping landscape photos separately from portraits) without being explicitly told what to look for.
This approach is particularly useful when data is abundant but unlabeled, or when you want to explore data without a prior hypothesis.
Read also: What is “Underfitting” in AI? Definition, explanations, and examples
Example of unsupervised learning and results
A classic example of unsupervised learning is clustering. Take the case of an e-commerce company that wants to better understand its customers. It has a large amount of purchase data, but without predefined categories.
A clustering algorithm, such as k-means, can analyze this data (for example, purchase frequency, product type, amount spent) and automatically group customers into similar segments, such as:
- Customers who frequently buy high-tech products.
- Customers who occasionally buy clothes.
- Customers who spend a lot in one go.
Result: The company gets groups of customers with similar behaviors, allowing it to personalize its marketing campaigns without having to manually define these categories beforehand.
Difference between supervised and unsupervised learning
To properly understand unsupervised learning, it is helpful to compare it to supervised learning:
- Supervised learning: The algorithm is trained on a labeled dataset, where each input is associated with a correct output. For example, to classify emails as “spam” or “not spam,” the algorithm is provided with examples of already labeled emails. The goal is to predict accurate results for new data.
- Unsupervised learning: There are no labels or expected answers. The algorithm explores the data to find structures or relationships on its own, such as grouping customers or detecting anomalies.
In summary, supervised learning is guided by annotated examples, while unsupervised learning is exploratory and autonomous.
AI uses based on unsupervised learning
Unsupervised learning is used in many fields to extract information from raw data. Here are some examples of applications:
- Market segmentation: As in the clustering example, companies group their customers to better target their campaigns.
- Anomaly detection: Banks use unsupervised learning to identify unusual transactions, such as potential fraud, by spotting behaviors that deviate from the norm.
- Dimensionality reduction: In image or text processing, algorithms like Principal Component Analysis (PCA) simplify complex data while retaining its essential characteristics.
- Recommendation systems: Platforms like Netflix or Spotify analyze user behavior to suggest similar content, even without explicit labels.
See also: What are GANs in AI, the “Generative Adversarial Networks”?
Typical task of unsupervised learning
A typical task of unsupervised learning is clustering, already mentioned, which involves grouping similar data based on its characteristics. Another common task is anomaly detection, where the algorithm identifies data points that do not match the majority patterns (for example, detecting a defect in a production line).
A third task is representation learning, where the algorithm transforms complex data (such as images or text) into a simpler form that can be used for other analyses, as in autoencoder neural networks.
Conclusion
Unsupervised learning is a powerful AI approach that allows for exploring and organizing data without the need for prior labels. By discovering hidden structures, it helps solve a variety of problems, from customer segmentation to fraud detection.
Compared to supervised learning, it offers unique flexibility for analyzing raw data, making it an essential tool in the world of AI. If you are working with unlabeled data or looking to explore unknown trends, unsupervised learning is a solution to consider.
