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Artificial Intelligence Review
Published in: Artificial Intelligence Review 2/2015

01-02-2015

An analytical review of XML association rules mining

Authors: Mohammad Moradi, Mohammad Reza Keyvanpour

Published in: Artificial Intelligence Review | Issue 2/2015

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Abstract

Over the past decade, there has been increasing interest in using extensible markup language (XML) which has made it a de facto standard for representing and exchanging data over different systems and platforms (specifically the internet). Due to the popularity of XML and with increasing numbers of XML documents, the process of knowledge discovery from this type of data has found more attention. Although in the last decade several different methods have been proposed for mining XML documents, this research field still is in its infancy compared to traditional data mining. As in relational techniques, in the case of XML documents, association rule mining has a strong research interest. In this paper we have performed a comprehensive study on all of the major works so far done on mining association rules from XML documents. The main contribution of the paper is to provide a reference point for future researches by collecting different techniques and methods concerning the topic; classifying them into a number of categories and creating a complete bibliography of the major published works. We think that this paper can help researchers in XML association rules mining domains to quickly find the current work as the basis for the future activities.
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Metadata
Title
An analytical review of XML association rules mining
Authors
Mohammad Moradi
Mohammad Reza Keyvanpour
Publication date
01-02-2015
Publisher
Springer Netherlands
Published in
Artificial Intelligence Review / Issue 2/2015
Print ISSN: 0269-2821
Electronic ISSN: 1573-7462
DOI
https://doi.org/10.1007/s10462-012-9376-5

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