An Overviewof Similarity Measures for Clustering XML Documents

An Overviewof Similarity Measures for Clustering XML Documents

Giovanna Guerrini (Universita degli Studi di Genova, Italy), Marco Mesiti (Universita degli Studi di Milano, Italy) and Ismael Sanz (Universitat Jaume I, Spain)
DOI: 10.4018/978-1-59904-228-2.ch003
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

The large amount and heterogeneity of XML documents on the Web require the development of clustering techniques to group together similar documents. Documents can be grouped together according to their content, their structure, and links inside and among documents. For instance, grouping together documents with similar structures has interesting applications in the context of information extraction, of heterogeneous data integration, of personalized content delivery, of access control definition, of web site structural analysis, of comparison of RNA secondary structures. Many approaches have been proposed for evaluating the structural and content similarity between tree-based and vector-based representations of XML documents. Link-based similarity approaches developed for Web data clustering have been adapted for XML documents. This chapter discusses and compares the most relevant similarity measures and their employment for XML document clustering.

Complete Chapter List

Search this Book:
Reset