Fast discovery of association rules
Advances in knowledge discovery and data mining
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
TreeFinder: a First Step towards XML Data Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Data Mining for Maximal Frequent Subtrees
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Efficiently Mining Frequent Trees in a Forest: Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
Efficient Mining of High Branching Factor Attribute Trees
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
TRIPS and TIDES: new algorithms for tree mining
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Mining Frequent Induced Subtrees by Prefix-Tree-Projected Pattern Growth
WAIMW '06 Proceedings of the Seventh International Conference on Web-Age Information Management Workshops
Frequent Subtree Mining - An Overview
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
PrefixTreeESpan: a pattern growth algorithm for mining embedded subtrees
WISE'06 Proceedings of the 7th international conference on Web Information Systems
Clustering XML Documents Using Closed Frequent Subtrees: A Structural Similarity Approach
Focused Access to XML Documents
HCX: an efficient hybrid clustering approach for XML documents
Proceedings of the 9th ACM symposium on Document engineering
Utilising semantic tags in XML clustering
INEX'09 Proceedings of the Focused retrieval and evaluation, and 8th international conference on Initiative for the evaluation of XML retrieval
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Frequent subtree mining has attracted a great deal of interest among the researchers due to its application in a wide variety of domains. Some of the domains include bio informatics, XML processing, computational linguistics, and web usage mining. Despite the advances in frequent subtree mining, mining for the entire frequent subtrees is infeasible due to the combinatorial explosion of the frequent subtrees with the size of the datasets. In order to provide a reduced and concise representation without information loss, we propose a novel algorithm, PCITMiner (Prefix-based Closed Induced Tree Miner). PCITMiner adopts the prefix-based pattern growth strategy to provide the closed induced frequent subtrees efficiently. The empirical analysis reveals that our algorithm significantly outperforms the current state of the art algorithm, PrefixTreeISpan(Zou, Lu, Zhang, Hu and Zhou 2006b).