Clustering
An unsupervised machine learning technique that groups completely unlabelled data points into distinct clusters based entirely on their underlying similarity.
Think of It Like This
Like dumping a massive pile of unmarked puzzle pieces onto a table and sorting them purely by color and pattern without knowing what the final picture is.
Because there is no ground truth or predefined categories to learn from, the algorithm relies on distance metrics like Euclidean distance to find natural groupings in the feature space. K-Means is the most famous example, aggressively partitioning data into circular blobs, while density-based algorithms like DBSCAN can find complex, irregularly shaped groups and automatically isolate noisy outliers.