The organization of a growing amount of data as well as the identification of relevant information therein is a current research challenge with increasing importance. This thesis contributes to a (semi-)automatic support for this task by personalized hierarchical structuring. The goal is the development of data mining methods that structure a collection automatically into a hierarchy. Since this can be done in several ways, the goal is furthermore to regard specific user preferences to discover the most suitable structure. These structuring preferences are, however, only given implicitly through previously organized personal data of the user. Two fundamentally different types of approaches are studied in detail. [.]
