XML clustering and retrieval through principal component analysis

Jason T.L. Wang, Jianghui Liu, Junhan Wang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

XML is increasingly important in data exchange and information management. A great deal of efforts have been spent in developing efficient techniques for storing, querying, indexing and accessing XML documents. In this paper we propose a new approach to clustering XML data. In contrast to previous work, which focused on documents defined by different DTDs, the proposed method works for documents with the same DTD. Our approach is to extract features from documents, modeled by ordered labeled trees, and transform the documents to vectors in a high-dimensional Euclidean space based on the occurrences of the features in the documents. We then reduce the dimensionality of the vectors by principal component analysis (PCA) and cluster the vectors in the reduced dimensional space. The PCA enables one to identify vectors with co-occurrent features, thereby enhancing the accuracy of the clustering. We also discuss an extension of our techniques to XML retrieval. Experimental results based on documents obtained from Wisconsin's XML data bank show the effectiveness and good performance of the proposed techniques.

Original languageEnglish (US)
Pages (from-to)683-699
Number of pages17
JournalInternational Journal on Artificial Intelligence Tools
Volume14
Issue number4
DOIs
StatePublished - Aug 2005

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Keywords

  • Data mining
  • Document clustering
  • Information retrieval

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