Fast algorithms for finding nearest common ancestors
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CLIP: concept learning from inference patterns
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SIAM Journal on Computing
Fast discovery of association rules
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Tree pattern matching and subset matching in deterministic O(n log3 n)-time
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Finding Patterns in Three-Dimensional Graphs: Algorithms and Applications to Scientific Data Mining
IEEE Transactions on Knowledge and Data Engineering
Approximate Graph Schema Extraction for Semi-Structured Data
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
Counting Twig Matches in a Tree
Proceedings of the 17th International Conference on Data Engineering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
LATIN '00 Proceedings of the 4th Latin American Symposium on Theoretical Informatics
Correlating XML data streams using tree-edit distance embeddings
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Efficiently mining frequent trees in a forest
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ANF: a fast and scalable tool for data mining in massive graphs
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Online Algorithms for Mining Semi-structured Data Stream
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
DTD-Miner: A Tool for Mining DTD from XML Documents
WECWIS '00 Proceedings of the Second International Workshop on Advance Issues of E-Commerce and Web-Based Information Systems (WECWIS 2000)
CloseGraph: mining closed frequent graph patterns
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TreeRank: a similarity measure for nearest neighbor searching in phylogenetic database
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
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BIO-AJAX: an extensible framework for biological data cleaning
ACM SIGMOD Record
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IEEE Transactions on Knowledge and Data Engineering
Efficiently Mining Frequent Trees in a Forest: Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
Frequent Subtree Mining - An Overview
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Discovering Frequent Agreement Subtrees from Phylogenetic Data
IEEE Transactions on Knowledge and Data Engineering
Efficient mining of frequent XML query patterns with repeating-siblings
Information and Software Technology
An integrated, generic approach to pattern mining: data mining template library
Data Mining and Knowledge Discovery
Mining Unordered Distance-Constrained Embedded Subtrees
DS '08 Proceedings of the 11th International Conference on Discovery Science
Split-Order Distance for Clustering and Classification Hierarchies
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Mining tree-structured data on multicore systems
Proceedings of the VLDB Endowment
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Mining frequent closed trees in evolving data streams
Intelligent Data Analysis - Ubiquitous Knowledge Discovery
Biomonitoring, phylogenetics and anomaly aggregation systems
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
Frequent Subtree Mining - An Overview
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Mining of closed frequent subtrees from frequently updated databases
Intelligent Data Analysis
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Frequent structure mining (FSM) aims to discover andextract patterns frequently occuring in structural data,such as trees and graphs.FSM finds many applications inbioinformatics, XML processing, Web log analysis, and soon.In this paper we present a new FSM technique for findingpatterns in rooted unordered labeled trees.The patternsof interest are cousin pairs in these trees.A cousin pair isa pair of nodes sharing the same parent, the same grand-parent,or the same great-grandparent, etc.Given a treeT, our algorithm finds all interesting cousin pairs of T inO(|T|2) time when |T| is the number of nodes in T.Experimentalresults on synthetic data and phylogenies showthe scalability and effectiveness of the proposed technique.To demonstrate the usefulness of our approach, we discussits applications to locating co-occurring patterns in multipleevolutionary trees, evaluating the consensus of equallyparsimonious trees, and finding kernel trees of groups ofphylogenies.We also describe extensions of our algorithmsfor undirected acyclic graphs (or free trees).