Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Fast discovery of association rules
Advances in knowledge discovery and data mining
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Efficient discovery of error-tolerant frequent itemsets in high dimensions
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Pruning Redundant Association Rules Using Maximum Entropy Principle
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on 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
Handling very large numbers of association rules in the analysis of microarray data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Indexing and Mining Free Trees
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
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
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Frequent Subtree Mining - An Overview
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
Finding low-entropy sets and trees from binary data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
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Tree structures are a natural way of describing occurrence relationships between attributes in a dataset. We define a new class of tree patterns for unordered 0–1 data and consider the problem of discovering frequently occurring members of this pattern class. Intuitively, a tree T occurs in a row u of the data, if the attributes of T that occur in u form a subtree of T containing the root. We show that this definition has advantageous properties: only shallow trees have a significant probability of occurring in random data, and the definition allows a simple levelwise algorithm for mining all frequently occurring trees. We demonstrate with empirical results that the method is feasible and that it discovers interesting trees in real data.