A methodology for clustering XML documents by structure

Theodore Dalamagas; Tao Cheng; Klaas-Jan Winkel; Timos Sellis

doi:10.1016/j.is.2004.11.009

A methodology for clustering XML documents by structure

Theodore Dalamagas, Tao Cheng, Klaas-Jan Winkel, Timos Sellis

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Abstract

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.

Original language	English
Pages (from-to)	187-228
Journal	Information systems
Volume	31
Issue number	3
DOIs	https://doi.org/10.1016/j.is.2004.11.009
Publication status	Published - 2006

Keywords

Tree edit distance
XML
Structural similarity
Clustering
Structural summary

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10.1016/j.is.2004.11.009

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title = "A methodology for clustering XML documents by structure",

abstract = "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.",

keywords = "Tree edit distance, XML, Structural similarity, Clustering, Structural summary",

author = "Theodore Dalamagas and Tao Cheng and Klaas-Jan Winkel and Timos Sellis",

note = "Klaas-Jan Winkel, Faculty of Computer Science, University of Twente",

year = "2006",

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AU - Dalamagas, Theodore

AU - Cheng, Tao

AU - Winkel, Klaas-Jan

AU - Sellis, Timos

N1 - Klaas-Jan Winkel, Faculty of Computer Science, University of Twente

PY - 2006

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N2 - 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.

AB - 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.

KW - Tree edit distance

KW - XML

KW - Structural similarity

KW - Clustering

KW - Structural summary

U2 - 10.1016/j.is.2004.11.009

DO - 10.1016/j.is.2004.11.009

M3 - Article

SN - 0306-4379

VL - 31

SP - 187

EP - 228

JO - Information systems

JF - Information systems

IS - 3

ER -

A methodology for clustering XML documents by structure

Abstract

Keywords

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