X3-Miner: Mining Patterns from XML Database

An XML enabled framework for the representation of association rules in databases was first presented in [4]. In Frequent Structure Mining (FSM), one of the popular approaches is to use graph matching that use data structures such as the adjacency matrix [7] or adjacency list [8]. Another approach represents semistructured tree-like structures using a string representation, which is more space efficient and relatively easy for manipulation [10]. 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 [10] 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.