Efficient mining of weighted association rules (WAR)
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining patterns from graph traversals
Data & Knowledge Engineering
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
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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, WTSPMinerdecompose 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 WTSPMineris efficient and scalable for finding weighted sequential patterns from weighted graph traversals.