Mining for paths in flow graphs
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
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In this paper, a transformable model of EWDG (Edge-Weighted Directed Graph) and VWDG (Vertex-Weighted Directed Graph) is proposed to resolve the problem of weighted traversal patterns mining. Based on the model, an effective algorithm called GTCWFPMiner (Graph Traversals-based Closed Weighted Frequent Patterns Miner) is presented. The algorithm exploits a divide-and-conquer paradigm with a pattern growth method to mine closed frequent patterns with weight constraint from the traversals on directed graph. It incorporates the closure property with weight constrains to reduce effectively search space and extracts succinct and lossless patterns from graph traversal TDB. Experimental results of synthetic data show that the algorithm is an efficient and scalable algorithm for mining closed weighted frequent patterns based on graph traversals.