Nowadays, the explosive growth in text data emphasizes the need for developing new and computationally efficient methods and credible theoretical support tailored for analyzing such large-scale data. Given the vast amount of this kind of unstructured data, the majority of it is not classified, hence unsupervised learning techniques show to be useful in this field. Document clustering has proven to be an efficient tool in organizing textual documents and it has been widely applied in different areas from information retrieval to topic modeling. Before introducing the proposals of document clustering algorithms, the principal steps of the whole process, including the mathematical representation of documents and the preprocessing phase, are discussed. Then, the main clustering algorithms used for text data are critically analyzed, considering prototype-based, graph-based, hierarchical, and model-based approaches. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification Statistical Learning and Exploratory Methods of the Data Sciences > Text Mining Data: Types and Structure > Text Data K E Y W O R D S document clustering, document representation, graph-based methods, hierarchical methods, model-based methods, prototype-based methods, text data