An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
DEMON: Mining and Monitoring Evolving Data
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Discovering frequently changing structures from historical structural deltas of unordered XML
Proceedings of the thirteenth ACM international conference on Information and knowledge management
XML structural delta mining: issues and challenges
Data & Knowledge Engineering - Special issue: ER 2003
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In this paper, we present a FASST mining approach to extract the frequently changing semantic structures (FASSTs), which are a subset of semantic substructures that change frequently, from versions of unordered XML documents. We propose a data structure, H-DOM+, and a FASST mining algorithm, which incorporates the semantic issue and takes the advantage of the related domain knowledge. The distinct feature of this approach is that the FASST mining process is guided by the user-defined concept hierarchy. Rather than mining all the frequent changing structures, only these frequent changing structures that are semantically meaningful are extracted. Our experimental results show that the H-DOM+ structure is compact and the FASST algorithm is efficient with good scalability. We also design a declarative FASST query language, FASSTQUEL, to make the FASST mining process interactive and flexible.