Clustering is the process of grouping similar document vectors together in a way that reflects underlying patterns or themes in your data. When you represent documents as vectors — using methods such as bag-of-words or TF-IDF weighting — each document becomes a point in a high-dimensional space. Clustering aims to discover groups of these points that are more similar to each other than to the rest, revealing structure within a collection of texts.

To build geometric intuition, imagine each document as a dot in a vast space where each dimension corresponds to a term in your vocabulary. In this space, clusters are dense regions: groups of document vectors that are close together, separated from other such groups by sparser regions. The proximity of these vectors indicates that the corresponding documents share similar word usage patterns, topics, or styles. Even though you cannot visualize spaces with thousands of dimensions, the principle remains — clusters form "islands" of similarity.

Clustering is especially useful in practical scenarios involving large document collections. For instance, in news aggregation, clustering can automatically group articles covering the same event, making it easier to organize and browse headlines. In digital libraries, clustering helps organize research papers by topic, assisting you in discovering related work. Customer feedback analysis can benefit by clustering reviews to identify common themes or issues. In each case, clustering reveals hidden structures that manual inspection would miss, enabling more efficient information retrieval and exploration.