PSpan:Mining Frequent Subnets of Petri Nets
This addresses a specific data mining need for Petri net analysis, but it is incremental as it adapts existing graph mining techniques to a new domain.
The paper tackles the problem of mining frequent complete subnets from a set of Petri nets by proposing the PSpan algorithm, which transforms nets into graphs for mining and back, with experiments confirming its reliability and complexity.
This paper proposes for the first time an algorithm PSpan for mining frequent complete subnets from a set of Petri nets. We introduced the concept of complete subnets and the net graph representation. PSpan transforms Petri nets in net graphs and performs sub-net graph mining on them, then transforms the results back to frequent subnets. PSpan follows the pattern growth approach and has similar complexity like gSpan in graph mining. Experiments have been done to confirm PSpan's reliability and complexity. Besides C/E nets, it applies also to a set of other Petri net subclasses.