Abstract
In this paper, we study an online data mining problem from streams of semi-structured data such as XML data. Modeling semi-structured data and patterns as labeled ordered trees, we present an online algorithm StreamT that receives fragments of an unseen possibly infinite semi-structured data in the document order through a data stream, and can return the current set of frequent patterns immediately on request at any time. We give modifications of the algorithm to other online mining models. Furthermore we implement our algorithms in different online models and candidate management strategies, then show empirical analyses to evaluate the algorithms. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
Asai, T., Abe, K., Kawasoe, S., Arimura, H., & Arikawa, S. (2007). Efficient algorithms for finding frequent substructures from semi-structured data streams. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3609 LNAI, pp. 29–45). Springer Verlag. https://doi.org/10.1007/978-3-540-71009-7_3
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