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.
|Titolo:||An Overview of Similarity Measures for Clustering XML Documents|
|Parole Chiave:||XML, data mining, web-based applications, retrieval|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Data di pubblicazione:||2006|
|Digital Object Identifier (DOI):||10.4018/978-1-59904-228-2|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|