Abstract
Data mining on graph traversals has been an active research during recent years. However, traditional traversal patterns mining algorithms only considered un-weighted traversals, and they are most un-sequential patterns mining algorithms. Based on the property that the traversal pattern and the items in it are consecutive, this paper regards a traversal patter as a particular sequential pattern and proposes a new algorithm, called WTSPMiner (WeightedTraversal-basedSequential Patterns Miner), to mine weightedsequential patterns from traversals on weighted directed graph. Adopting an improved weighted prefix-projected pattern growth approach, WTSPMiner decompose the task of mining original sequence database into a series of smaller tasks of mining locally projected database so as to efficiently discover fewer but important weighted sequential patterns. Comprehensive experimental results show that WTSPMiner is efficient and scalable for finding weighted sequential patterns from weighted graph traversals.
This work was supported in part by the Natural Science Fund of Shandong Province (No.Y2007G25), the Excellent Young Scientist Foundation of Shandong Province, China (No.2006BS01017), and the Scientific Research Development Project of Shandong Provincial Education Department, China (No. J06N06).
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Geng, R., Dong, X., Jiang, H., Xu, W. (2008). WTSPMiner: Efficiently Mining Weighted Sequential Patterns from Directed Graph Traversals. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, vol 5226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87442-3_47
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