Mining Association Rules from XML Documents

Mining Association Rules from XML Documents

Laura Irina Rusu (La Trobe University, Australia), Wenny Rahayu (La Trobe University, Australia) and David Taniar (Monash University, Australia)
DOI: 10.4018/978-1-61692-852-0.ch321
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Abstract

This chapter presents some of the existing mining techniques for extracting association rules out of XML documents in the context of rapid changes in the Web knowledge discovery area. The initiative of this study was driven by the fast emergence of XML (eXtensible Markup Language) as a standard language for representing semistructured data and as a new standard of exchanging information between different applications. The data exchanged as XML documents become richer and richer every day, so the necessity to not only store these large volumes of XML data for later use, but to mine them as well to discover interesting information has became obvious. The hidden knowledge can be used in various ways, for example, to decide on a business issue or to make predictions about future e-customer behaviour in a Web application. One type of knowledge that can be discovered in a collection of XML documents relates to association rules between parts of the document, and this chapter presents some of the top techniques for extracting them.

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