Querying XML Documents by Using Association Rules

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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Chi-Square Test for Anomaly Detection in XML Documents Using Negative Association Rules

Computer and Information Science ◽

10.5539/cis.v2n1p35 ◽

2009 ◽

Vol 2 (1) ◽

Cited By ~ 1

Author(s):

K. Premalatha ◽

A.M. Natarajan

Keyword(s):

Anomaly Detection ◽

Association Rules ◽

Negative Association ◽

Chi Square ◽

Xml Documents ◽

Chi Square Test ◽

Negative Association Rules

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Extracting association rules from XML documents using XQuery

Proceedings of the fifth ACM international workshop on Web information and data management - WIDM '03 ◽

10.1145/956699.956720 ◽

2003 ◽

Cited By ~ 22

Author(s):

Jacky W. W. Wan ◽

Gillian Dobbie

Keyword(s):

Association Rules ◽

Xml Documents

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Mining Association Rules from XML Documents

Services and Business Computing Solutions with XML ◽

10.4018/978-1-60566-330-2.ch011 ◽

2010 ◽

pp. 176-196

Author(s):

Laura Irina Rusu ◽

Wenny Rahayu ◽

David Taniar

Keyword(s):

Knowledge Discovery ◽

Association Rules ◽

Web Application ◽

Semistructured Data ◽

Markup Language ◽

Xml Documents ◽

Rapid Changes ◽

Extensible Markup ◽

Hidden Knowledge ◽

The Web

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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Mining Association Rules from XML Documents

Web Data Management Practices ◽

10.4018/978-1-59904-228-2.ch004 ◽

2007 ◽

pp. 79-103 ◽

Cited By ~ 4

Author(s):

Laura Irina Rusu ◽

Wenny Rahayu ◽

David Taniar

Keyword(s):

Knowledge Discovery ◽

Association Rules ◽

Large Volume ◽

Web Application ◽

Markup Language ◽

Xml Documents ◽

Rapid Changes ◽

Extensible Markup ◽

Hidden Knowledge ◽

The Web

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 semi-structured data and as a new standard of exchanging information between different applications. The data exchanged as XML documents becomes every day richer and richer, so the necessity to not only store these large volume 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 which 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.

Download Full-text