RAKING:An Efficient K-Maximal Frequent Pattern Mining Algorithm on Uncertain Graph Database
Meng Han · Chinese Journal of Computers · 2010
An uncertain graph can represent a large number of possible graph instances.This greatly reduces the efficiency of existing frequent pattern mining algorithms.The paper proposes a random walk based K-maximal frequent pattern mining algorithm on uncertain graph set.Firstly,each uncertain graph is converted to a graph without uncertain information.Candidate frequent patterns are retrieved from the converted graph set.Then,the candidate frequent patterns are transformed to corresponding uncertain graph pattern and searching space of maximal frequent patterns are constructed as well.Finally,K-maximal frequent patterns are selected from all maximal frequent patterns equiprobably.Theoretical analysis and experimental results show that the proposed algorithm can efficiently retrieve the K-maximal frequent patterns of an uncertain graph set.