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
Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Closed and Maximal Frequent Subtrees from Databases of Labeled Rooted Trees
IEEE Transactions on Knowledge and Data Engineering
AMIOT: Induced Ordered Tree Mining in Tree-Structured Databases
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Data Mining and Knowledge Discovery
Efficiently Mining Frequent Embedded Unordered Trees
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Frequent Subtree Mining - An Overview
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Efficient Mining of Closed Induced Ordered Subtrees in Tree-structured Databases
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Mining Frequent Induced Subtree Patterns with Subtree-Constraint
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Efficiently mining closed constrained frequent ordered subtrees by using border information
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
A statistical interestingness measures for XML based association rules
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
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In this paper, in order to discover significant patterns, we focus on the problem of mining frequent mutually dependent ordered subtrees , i.e. frequent ordered subtrees in which all building blocks are mutually dependent, in tree databases. While three kinds of mutually dependent ordered subtrees are considered based on the building blocks used, we propose efficient breadth-first algorithms for each kind of subtrees. The effectiveness of the proposed framework is assessed through the experiments with synthetic and real world datasets.