Description: Document clustering is a fundamental technique in the field of information processing and data mining, which involves organizing a set of documents in such a way that the documents within the same group are more similar to each other than to those in other groups. This similarity can be measured through various characteristics, such as textual content, structure, context, or even multimodal elements like images and graphics. The main goal of this technique is to facilitate the search, retrieval, and analysis of information, allowing users to identify patterns, trends, and relationships within large volumes of data. Clustering can be performed using machine learning algorithms that employ techniques such as unsupervised learning, where distance metrics are applied to determine the closeness between documents. This methodology is especially relevant in a world where the amount of available information is growing exponentially, making the organization and categorization of data essential for effective management. Furthermore, document clustering can be used in various applications, from content recommendation to the organization of digital libraries, enhancing accessibility and user experience in interacting with information.
