Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Generating association rules from semi-structured documents using an extended concept hierarchy
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Mining Association Rules from XML Data
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
A Tool for Extracting XML Association Rules
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
Extracting association rules from XML documents using XQuery
WIDM '03 Proceedings of the 5th ACM international workshop on Web information and data management
Mining Maximally Common Substructures from XML Trees with Lists-Based Pattern-Growth Method
CIS '07 Proceedings of the 2007 International Conference on Computational Intelligence and Security
Tree model guided candidate generation for mining frequent subtrees from XML documents
ACM Transactions on Knowledge Discovery from Data (TKDD)
Mining interesting XML-enabled association rules with templates
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
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XML is increasingly popular for knowledge representations. However, mining association rules from them is a challenging issue since XML data is usually poorly supported by the current database systems due to its tree structure. Several encouraging attempts at developing methods for mining rules in tree dataset have been proposed, but simplicity and efficiency still remain significant impediments for further development. What is needed is a clear and simple methodology for finding the rules that are hidden in the heterogeneous tree data. In this paper, we adjust and fine-tune the label projection method which has been recently published to compute association rules from trees. The suggested approach avoids the computationally intractable problem caused by the number of nodes contained in the tree dataset.