Combining structure and content similarities for XML document clustering
Tran, Tien, Nayak, Richi, & Bruza, Peter D. (2008) Combining structure and content similarities for XML document clustering. In 7th Australasian Data Mining Conference, 27-28 November 2008, Glenelg, South Australia.
This paper proposes a clustering approach that explores both the content and the structure of XML documents for determining similarity among them. Assuming that the content and the structure of XML documents play different roles and importance depending on the use and purpose of a dataset, the content and structure information of the documents are handled using two different similarity measuring methods. The similarity values produced from these two methods are then combined with weightings to measure the overall document similarity. The effect of structure similarity and content similarity on the clustering solution is thoroughly analysed. The experiments prove that clustering of the text-centric XML documents based on the content-only information produces a better solution in a homogeneous environment, documents that derived from one structural definition; however, in a heterogeneous environment, documents that derived from two or more structural definitions, clustering of the text-centric XML documents produces a better result when the structure and the content similarities of the documents are combined with different strengths.
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Keywords:||XML, Clustering, Latent Semantic Kernel, Vector Space Model|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright 2008 Australian Computer Society|
|Copyright Statement:||Reproduction for academic, not-for profit purposes permitted provided this text is included.|
|Deposited On:||26 Feb 2009 14:50|
|Last Modified:||29 Feb 2012 23:48|
Repository Staff Only: item control page