Document clustering is a technique in data analysis that groups a set of documents into clusters based on their content similarity. This process helps in organizing large collections of text, making it easier to retrieve and analyze information. By using algorithms, such as k-means or hierarchical clustering, documents that share similar themes or topics are placed together, allowing for better insights and understanding. The applications of document clustering are vast, including search engines, recommendation systems, and topic modeling. For instance, in a search engine, clustering can improve the relevance of search results by grouping similar articles or web pages, enhancing the user experience and information retrieval efficiency.