Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Closed and Maximal Frequent Subtrees from Databases of Labeled Rooted Trees
IEEE Transactions on Knowledge and Data Engineering
Frequent Subtree Mining - An Overview
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
PCITMiner: prefix-based closed induced tree miner for finding closed induced frequent subtrees
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
XCFS: an XML documents clustering approach using both the structure and the content
Proceedings of the 18th ACM conference on Information and knowledge management
Reverse extraction of protocol model from network applications
International Journal of Internet Protocol Technology
Mining of closed frequent subtrees from frequently updated databases
Intelligent Data Analysis
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Frequent embedded subtree pattern mining is an important data mining problem with broad applications. In this paper, we propose a novel embedded subtree mining algorithm, called PrefixTreeESpan (i.e. Prefix-Tree-projected Embedded-Subtree pattern), which finds a subtree pattern by growing a frequent prefix-tree. Thus, using divide and conquer, mining local length-1 frequent subtree patterns in Prefix-Tree-Projected database recursively will lead to the complete set of frequent patterns. Different fromChopper and XSpanner [4], PrefixTreeESpan does not need a checking process. Our performance study shows that PrefixTreeESpan outperforms Apriori-like algorithm: TreeMiner [6], and pattern-growth algorithms :Chopper , XSpanner .