  Modern software systems are able to record vast amounts of user actions,
stored for later analysis. One of the main types of such user interaction data
is click data: the digital trace of the actions of a user through the graphical
elements of an application, website or software. While readily available, click
data is often missing a case notion: an attribute linking events from user
interactions to a specific process instance in the software. In this paper, we
propose a neural network-based technique to determine a case notion for click
data, thus enabling process mining and other process analysis techniques on
user interaction data. We describe our method, show its scalability to datasets
of large dimensions, and we validate its efficacy through a user study based on
the segmented event log resulting from interaction data of a mobility sharing
company. Interviews with domain experts in the company demonstrate that the
case notion obtained by our method can lead to actionable process insights.
