Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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
A Tool for Extracting XML Association Rules
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
Detecting Changes in XML Documents
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
XRules: an effective structural classifier for XML data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
XML structural delta mining: issues and challenges
Data & Knowledge Engineering - Special issue: ER 2003
Monitoring data dependencies in concurrent process execution through delta-enabled grid services
International Journal of Web and Grid Services
In the Search of NECTARs from Evolutionary Trees
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
X-Tracking the Changes of Web Navigation Patterns
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Maintaining versions of dynamic XML documents
WISE'05 Proceedings of the 6th international conference on Web Information Systems Engineering
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Mining changes from versions of dynamic XML documents
KDXD'06 Proceedings of the First international conference on Knowledge Discovery from XML Documents
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Recently, a large amount of work has been done in XML data mining. However, we observed that most of the existing works focus on the snapshot XML data, while XML data is dynamic in real applications. To the best of our knowledge, none of the existing works has addressed the issue of mining the history of changes to XML documents. Such mining results can be useful in many applications such as XML change detection, XML indexing, association rule mining, and classification etc. In this paper, we propose a novel approach to discover the frequently changing structures from the sequence of historical structural deltas of unordered XML. To make the structure discovering process efficient, an expressive and compact data model, Historical-Document Object Model (H-DOM), is proposed. Using this model, two basic algorithms, which can discover all the frequently changing structures with only two scans of the XML sequence, are presented. Experimental results show that our algorithms, together with the optimization techniques, are efficient and scalable.