myCADI is a machine learning framework associated with a graphical interface for discovering and understanding the internal structure of an unsupervised dataset. It is an intuitive end-user interface to the CADI approach [9], which uses a revised version of the Isolation Forest (IF) method to both 1) identify local anomalies, 2) reconstruct the cluster-based internal structure of the data, and 3) provide end-users with explanations of how anomalies deviate from the found clusters. myCADI takes numerical data as input and is structured around several interfaces, each of which displays a ranked list of the found anomalies, a description of the subspaces in which the different clusters lie, and feature attribution explanations to ease the interpretation of anomalies. These explanations make explicit why a selected point is considered to be a local anomaly of one (or more) cluster(s). The framework also provides dataset and trees visualizations.