Unsupervised learning is a type of machine learning that deals with data without labeled responses.
It is used when the goal is to identify hidden patterns or intrinsic structures in input data. Unsupervised learning algorithms are commonly applied in clustering, association, and dimensionality reduction tasks. These algorithms work by analyzing the data and finding patterns or groupings without any prior training or labeled data.
For example, in clustering, an unsupervised algorithm might group customers based on purchasing behavior without knowing the categories beforehand. In dimensionality reduction, it might simplify data by reducing the number of variables while retaining essential information.
Unsupervised learning is crucial because it allows for the discovery of patterns and structures in data that might not be immediately apparent.
- Alias
- Related terms
- Clustering Dimensionality Reduction Anomaly Detection