The process of dividing a collection of documents into groups, known as clusters, based on their similarity. Each cluster gathers documents that are more alike to each other than to those in different clusters. This method is used to identify inherent structures or patterns within the data, without prior knowledge of the group assignments. Clustering is a form of unsupervised learning, meaning it discovers natural groupings within the data based on the features and content of the documents. It’s a powerful tool for data analysis, helping in the organization, summarization, and exploration of large datasets. Applications of clustering include topic discovery, pattern recognition, and information retrieval, making it a fundamental technique in data science and machine learning for enhancing the understanding and management of complex information.