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XML clustering: a review of structural approaches

Published online by Cambridge University Press:  29 October 2014

Maciej Piernik ,
Dariusz Brzezinski ,
Tadeusz Morzy  and
Anna Lesniewska
Maciej Piernik
Affiliation:
Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland; e-mail: mpiernik@cs.put.poznan.pl, dbrzezinski@cs.put.poznan.pl, tmorzy@cs.put.poznan.pl, alesniewska@cs.put.poznan.pl
Dariusz Brzezinski
Affiliation:
Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland; e-mail: mpiernik@cs.put.poznan.pl, dbrzezinski@cs.put.poznan.pl, tmorzy@cs.put.poznan.pl, alesniewska@cs.put.poznan.pl
Tadeusz Morzy
Affiliation:
Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland; e-mail: mpiernik@cs.put.poznan.pl, dbrzezinski@cs.put.poznan.pl, tmorzy@cs.put.poznan.pl, alesniewska@cs.put.poznan.pl
Anna Lesniewska
Affiliation:
Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland; e-mail: mpiernik@cs.put.poznan.pl, dbrzezinski@cs.put.poznan.pl, tmorzy@cs.put.poznan.pl, alesniewska@cs.put.poznan.pl
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Abstract

With its presence in data integration, chemistry, biological, and geographic systems, eXtensible Markup Language (XML) has become an important standard not only in computer science. A common problem among the mentioned applications involves structural clustering of XML documents—an issue that has been thoroughly studied and led to the creation of a myriad of approaches. In this paper, we present a comprehensive review of structural XML clustering. First, we provide a basic introduction to the problem and highlight the main challenges in this research area. Subsequently, we divide the problem into three subtasks and discuss the most common document representations, structural similarity measures, and clustering algorithms. In addition, we present the most popular evaluation measures, which can be used to estimate clustering quality. Finally, we analyze and compare 23 state-of-the-art approaches and arrange them in an original taxonomy. By providing an up-to-date analysis of existing structural XML clustering algorithms, we hope to showcase methods suitable for current applications and draw lines of future research.

Type
Articles
Information
The Knowledge Engineering Review , Volume 30 , Issue 3 , May 2015 , pp. 297 - 323
Copyright
© Cambridge University Press 2014 

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