An efficient mining algorithm for maximal frequent patterns in uncertain graph database
What is it about?
Mining maximal frequent patterns is significant in many fields, but the mining efficiency is often low. The bottleneck lies in too many candidate subgraphs and extensive subgraph isomorphism tests. In this paper we propose an efficient mining algorithm. There are two key ideas behind the proposed methods. The first is to divide each edge of every certain graph (converted from equivalent uncertain graph) and build search tree, avoiding too many candidate subgraphs. The second is to search the tree built in the first step in order, avoiding extensive subgraph isomorphism tests.
Why is it important?
In many fields (e.g. wireless sensor network (WSN), protein-protein interaction(PPI) network, intelligent transportation system, the social network, road network etc.), a lot of key information is often included in frequent patterns, so the work efficiency can be greatly improved if many frequent subgraph patterns are mined effectively.