Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
DRYADE: A New Approach for Discovering Closed Frequent Trees in Heterogeneous Tree Databases
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Efficiently Mining Frequent Embedded Unordered Trees
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
An output-polynomial time algorithm for mining frequent closed attribute trees
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
TRIPS and TIDES: new algorithms for tree mining
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
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
An Experimental Comparison of Different Inclusion Relations in Frequent Tree Mining
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Mining tree-structured data on multicore systems
Proceedings of the VLDB Endowment
Frequent tree pattern mining: A survey
Intelligent Data Analysis
An Experimental Comparison of Different Inclusion Relations in Frequent Tree Mining
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
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In this paper, we present a new tree mining algorithm, DRYADEPARENT, based on the hooking principle first introduced in DRYADE [9]. In the experiments, we demonstrate that the branching factor and depth of the frequent patterns to find are key factor of complexity for tree mining algorithms. We show that DRYADEPARENT outperforms the current fastest algorithm, CMTreeMiner, by orders of magnitude on datasets where the frequent patterns have a high branching factor.