Generic clustering algorithms

• #Introduction
• #Worked examples
• #Related Drivers

Introduction

Worked examples

Related Drivers

• org.lcsim.recon.cluster.nn.NearestNeighborClusterDriver: This is a general-purpose clusterer. It uses a nearest-neighbor approach, starting from seed points and recursively adding nearby hits, where "nearby" is defined in terms of numbers of cells in each direction. The driver looks up every List<CalorimeterHit> in the event and applies the clusterer to each, writing out the clustered hits as a List<Cluster>. The name of each output list is the name of the input list plus a common extension (default is "NearestNeighborClusterDriver").
• org.lcsim.recon.cluster.mst.MSTClusterDriver: This is another general-purpose clusterer. It also works with a nearest-neighbor approach, but with a user-definable metric. The default metric is 3D distance between the centers of the two hit cells. This is in general more computationally expensive than the NN clusterer, but is less sensitive to changes in the detector geometry. The driver also has hooks to let the user veto certain kinds of hits or clusters.
• Directed Tree clusterer