Complete Mining of Frequent Patterns from Graphs: Mining Graph Data
Machine Learning
Discovering Structural Association of Semistructured Data
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficient Data Mining for Maximal Frequent Subtrees
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Discovering interesting information in XML data with association rules
Proceedings of the 2003 ACM symposium on Applied computing
Extracting association rules from XML documents using XQuery
WIDM '03 Proceedings of the 5th ACM international workshop on Web information and data management
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Advances in frequent itemset mining implementations: report on FIMI'03
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
An Efficient Algorithm for Discovering Frequent Subgraphs
IEEE Transactions on Knowledge and Data Engineering
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 Trees in a Forest: Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
Relational computation for mining association rules from XML data
Proceedings of the 14th ACM international conference on Information and knowledge management
Answering XML queries by means of data summaries
ACM Transactions on Information Systems (TOIS)
DryadeParent, An Efficient and Robust Closed Attribute Tree Mining Algorithm
IEEE Transactions on Knowledge and Data Engineering
Mining induced and embedded subtrees in ordered, unordered, and partially-ordered trees
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
A new method for mining association rules from a collection of XML documents
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part II
| Hi-index | 0.00 |
The increasing amount of very large XML datasets available to casual users is a challenging problem for our community, and calls for an appropriate support to efficiently gather knowledge from these data. Data mining, already widely applied to extract frequent correlations of values from both structured and semi-structured datasets, is the appropriate field for knowledge elicitation. In this work we describe an approach to extract Tree-based association rules from XML documents. Such rules provide approximate, intensional information on both the structure and the content of XML documents, and can be stored in XML format to be queried later on. A prototype system demonstrates the effectiveness of the approach.