The cluster hypothesis states that “closely associated documents tend to be relevant to the same requests” [45]. This is one of the most fundamental and influential hypotheses in the field of information retrieval and has given rise to a huge body of work. In this tutorial we will present the research topics that have emerged based on the cluster hypothesis. Specific focus will be placed on cluster-based document retrieval, the use of topic models for ad hoc IR, and the use of graph-based methods that utilize inter-document similarities. Furthermore, we will provide an in-depth survey of the suite of retrieval methods that rely, either explicitly or implicitly, on the cluster hypothesis and which are used for a variety of different tasks; e.g., query expansion, query-performance prediction, fusion and federated search, and search results diversification.