Abstract
With the sheer amount of data stored, presented and exchanged using XML nowadays, the ability to extract knowledge from XML data sources becomes increasingly important and desirable. This paper aims to integrate the newly emerging XML technology with data mining technology, using association rule mining as a case in point. Compared with traditional association mining in the well-structured world (e.g., relational databases), mining from XML data is faced with more challenges due to the inherent flexibilities of XML in both structure and semantics. The primary challenges include 1) a more complicated hierarchical data structure; 2) an ordered data context; and 3) a much bigger data size. To tackle these challenges, in this paper, we propose an extended XML-enabled association rule framework, which is flexible and powerful enough to represent both simple and complex structured association relationships inherent in XML data.
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© 2003 Springer-Verlag Berlin Heidelberg
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Feng, L., Dillon, T., Weigand, H., Chang, E. (2003). An XML-Enabled Association Rule Framework. In: Mařík, V., Retschitzegger, W., Štěpánková, O. (eds) Database and Expert Systems Applications. DEXA 2003. Lecture Notes in Computer Science, vol 2736. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45227-0_10
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DOI: https://doi.org/10.1007/978-3-540-45227-0_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40806-2
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