FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficiently Computing Frequent Tree-Like Topology Patterns in a Web Environment
TOOLS '99 Proceedings of the 31st International Conference on Technology of Object-Oriented Language and Systems
Itemset Trees for Targeted Association Querying
IEEE Transactions on Knowledge and Data Engineering
IncSpan: incremental mining of sequential patterns in large database
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
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In this paper, we propose a novel data mining scheme to explore the frequent hierarchical structure patterns, named tree-like patterns, with the relationship of each item on a sequence. By tree-like patterns, we are clear to find out the relation of items between the cause and effect. Finally, we discuss the different characteristics to our mined patterns with others. As a consequence, we can find out that our addressed tree-like patterns can be widely used to explore a variety of different applications.