Detecting anomalous longitudinal associations through higher order mining
AIDM '07 Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining - Volume 84
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This paper addresses the problem of generalizing temporal data based on calendar (date and time) attributes. The proposed method is based on a domain generalization graph, i.e., a lattice defining a partial order that represents a set of generalization relations for the attribute. We specify the components of a domain generalization graph suited to calendar attributes. We define granularity, subset, lookup, and algorithmic methods for specifying generalizations between calendar domains. To reduce the size of the domain generalization graph used in generalization and the number of results shown to the user, we use six types of pruning: reachability pruning, preliminary manual pruning, data range pruning, previous discard pruning, pregeneralization manual pruning, and post generalization pruning.