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Point patterns are sets or multi-sets of unordered elements that can be foundin numerous data sources. However, in data analysis tasks such asclassification and novelty detection, appropriate statistical models for pointpattern data have not received much attention. This paper proposes themodelling of point pattern data via random finite sets (RFS). In particular, wepropose appropriate likelihood functions, and a maximum likelihood estimatorfor learning a tractable family of RFS models. In novelty detection, we proposenovel ranking functions based on RFS models, which substantially improveperformance.

Model-based Classification and Novelty Detection For Point Pattern Data