KDD-Cup 2000 organizers' report: peeling the onion
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Algorithms on Trees and Graphs
Algorithms on Trees and Graphs
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Indexing and Mining Free Trees
ICDM '03 Proceedings of the Third 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
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
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
Frequent Subtree Mining - An Overview
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
On horn axiomatizations for sequential data
ICDT'05 Proceedings of the 10th international conference on Database Theory
An output-polynomial time algorithm for mining frequent closed attribute trees
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
An Experimental Comparison of Different Inclusion Relations in Frequent Tree Mining
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Information Sciences: an International Journal
Key roles of closed sets and minimal generators in concise representations of frequent patterns
Intelligent Data Analysis
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Many knowledge representation mechanisms consist of link-based structures; they may be studied formally by means of unordered trees. Here we consider the case where labels on the nodes are nonexistent or unreliable, and propose data mining processes focusing on just the link structure. We propose a representation of ordered trees, describe a combinatorial characterization and some properties, and use them to propose an efficient algorithm for mining frequent closed subtrees from a set of input trees. Then we focus on unordered trees, and show that intrinsic characterizations of our representation provide for a way of avoiding the repeated exploration of unordered trees, and then we give an efficient algorithm for mining frequent closed unordered trees.