Discovering Frequent Substructures from Hierarchical Semi-structured Data
Gao Cong, Lan Yi, Bing Liu, Ke Wang · 2002
Frequent substructure discovery from a collection of semi-structured objects can serve for storage, browsing, querying, indexing and classification of semi-structured documents. This paper examines the problem of discovering frequent substructures from a collection of hierarchical semi-structured objects of the same type. The use of wildcard is an important aspect of substructure discovery from semi-structured data due to the irregularity and lack of fixed structure of such data. This paper proposes a more general and powerful wildcard mechanism, which allows us to find more complex and interesting substructures than existing techniques. Furthermore, the complexity of structural information of semi-structured data and the usage of wildcard make the existing frequent set mining algorithms inapplicable for substructure discovery. In this work, we adopt a vertical format for the storage of semi-structured objects, and adapt a frequent set mining algorithm for our purpose. The application of our approach to real-life data shows that it is very effective.