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### Clustering  Having determined the events in each pixel it is possible to reconstruct the image with reduced noise levels using the matrix \(E\). However, this will not tell us anything about the properties of actual  release events (number, dynamics) (e.g., spark/wave numbers or properties)  as these macroscopic events  are made up of several events from different pixels. Therefore it It  is therefore  necessary to combine elementary  events from various pixels into macroscopic release events. This is achieved using the clustering method DBSCAN \cite{Sander_1998}. The method works in the parameter space and  findsa  clusters of arbitrary shape based on the density of events in parameter space. events.  This is preferable to standard clustering methods which often yield radially symmetric clusters (k-means, etc). Clustering is performed twice. First pixel events are clustered accoring to their shape i.e., clustering is done on matrix \(E_s\). This step distributes pixels into several groups based on solely their shape. For example, into shape (e.g., groups of elementary events composing  spark and wave events. events).  In the second clustering step, the \(E^p\) matrix is cluster for  each shape group is clustered based on location. This way, and physically nearby clusters of similar events are obtained. With this two-step approach,  release events of various types  consisting on elementary  events from multiple pixels are obtained.