\(\lambda \) -DBSCAN: Augmenting DBSCAN with Prior Knowledge

State-of-the-art density based cluster algorithms offer remarkable speed and robustness. However, they do not allow the user to make local changes without affecting the global outcome. The user thus has to choose between clustering a local region well or keeping the global result. We present a new approach, \(\lambda \) -DBSCAN, which augments the DBSCAN algorithm to include local a priori knowledge. The parameters can be specified per observation, rather than globally, which enables the user to include local knowledge about the data without modifying other regions. Furthermore, we define regions in the data that should be affected by certain parameter choices, to reduce the workload for a user.