The next step in HiMap is to perform clustering with density-based spatial clustering, DBSCAN (Fig. ) Clustering in HiMap requires a few inputs. One required input is the distance matrix which is calculated by HiMap using distance = 1similarity. Clustering by DBSCAN also depends on two additional parameters, the neighbor distance, 𝜖, and the minimal number of samples that can define a cluster, 𝑚𝑖𝑛 𝑠 . HiMap automates the calculation of 𝜖 and reports a default value and a suggested reasonable range as described below (Fig. ). For the 𝑚𝑖𝑛 𝑠 input parameter, the default value in HiMap is 2, so that clusters necessarily include an edge. A user can control both input parameters with keyword parameters.