A methodology for clustering XML documents by structure

The processing and management of XML data are popular research issues. However, operations based on the structure of XML data have not received strong attention. These operations involve, among others, the grouping of structurally similar XML documents. Such grouping results from the application of clustering methods with distances that estimate the similarity between tree structures. This paper presents a framework for clustering XML documents by structure. Modeling the XML documents as rooted ordered labeled trees, we study the usage of structural distance metrics in hierarchical clustering algorithms to detect groups of structurally similar XML documents. We suggest the usage of structural summaries for trees to improve the performance of the distance calculation and at the same time to maintain or even improve its quality. Our approach is tested using a prototype testbed. r 2004 Elsevier B.V. All rights reserved.

• Type: Journal
• Year: 2006
• Where: IS
• Authors: Theodore Dalamagas, Tao Cheng, Klaas-Jan Winkel, Timos K. Sellis
• Keywords: Artificial Intelligence, IS 2006, Similar Xml Documents, XML Data, XML Documents