An XML enabled framework for the representation of association rules in databases was first presented in .
In Frequent Structure Mining (FSM), one of the popular approaches is to use graph matching that use data structures such as the adjacency matrix  or adjacency list .
Another approach represents semistructured tree-like structures using a string representation, which is more space efficient and relatively easy for manipulation .
However, with XML, mining association rules are faced with more challenges due to the inherent flexibilities in both structure and semantics, such as: 1) more complicated hierarchical data structure; 2) ordered data context; and 3) much bigger data size.
To tackle these challenges, we propose an approach, X3-Miner, that efficiently extracts patterns from a large XML data set, and overcomes the challenges by: (1) exploring the use of a model validating approach in deducing the number of candidates generated by taking into account the semantics embedded in the tree-like structure in an XML database and obtain only valid candidates out of the XML database; (2) minimising I/O overhead by intersecting XML database with the frequent 1-itemset.
This results in a frequent 1-itemset XML tree.
The algorithm also progressively trims infrequent k-itemsets that contain infrequent (k-1)-itemsets; (3) extending the notion of string representation of a tree structure proposed in  to xstring for describing an XML document without loss of both structure and semantics.
Such an extension enables an easier traversal of the tree-structured XML data during our model-validating candidate generation.
Our experiments with both synthetic and real-life data sets demonstrate the effectiveness of the proposed model-validating approach in mining XML data.
|Title of host publication||Data Mining VI: Data Mining, Text Mining and their Business Applications|
|Place of Publication||Ashurst, Southampton, UK|
|Number of pages||10|
|Publication status||Published - 2005|
|Name||Information and Communication Technologies|
- DB-DM: DATA MINING