Finding hidden patterns in unlabeled data, such as clustering.
Unsupervised learning is a type of machine learning where the algorithm is given data without any explicit labels or correct outputs. The goal is to explore the data and find some inherent structure or pattern within it. Unlike supervised learning, there is no 'teacher' providing the right answers; the algorithm must learn on its own. This makes it a powerful tool for data exploration and discovery. One of the most common tasks in unsupervised learning is clustering. Clustering algorithms aim to group a set of data points in such a way that points in the same group (or 'cluster') are more similar to each other than to those in other groups. This is useful for tasks like customer segmentation, where a business might want to group customers based on their purchasing behavior to create targeted marketing campaigns. Another important unsupervised task is dimensionality reduction. This involves reducing the number of random variables (or features) under consideration, either by selecting a subset of the original features or by transforming the data into a lower-dimensional space. This can be useful for data visualization (as it's hard to visualize data with more than three dimensions) and for improving the performance of other machine learning algorithms by removing redundant or noisy features. Association rule learning is another key area, where the goal is to discover interesting relationships or 'association rules' among variables in large datasets. A classic example is market basket analysis, which might find that customers who buy diapers are also very likely to buy beer.