FAT-CAT : frequent attributes tree based classification

J. Knijf, de

doi:10.1007/978-3-540-73888-6_45

FAT-CAT : frequent attributes tree based classification

J. Knijf, de

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

13 Citations (Scopus)

Abstract

The natural representation of XML data is to use the underlying tree structure of the data. When analyzing these trees we are ensured that no structural information is lost. These tree structures can be efficiently analyzed due to the existence of frequent pattern mining algorithms that works directly on tree structured data. In this work we describe a classification method for XML data based on frequent attribute trees. From these frequent patterns we select so called emerging patterns, and use these as binary features in a decision tree algorithm. The experimental results show that combining emerging attribute tree patterns with standard classification methods, is a promising combination to tackle the classification of XML documents.

Original language	English
Title of host publication	Comparative Evaluation of XML Information Retrieval Systems (5th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2006, Dagstuhl Castle, Germany, December 17-20, 2006, Revised and Selected Papers)
Editors	N. Fuhr, M. Lalmas, A. Trotman
Place of Publication	Berlin
Publisher	Springer
Pages	485-496
ISBN (Print)	978-3-540-73887-9
DOIs	https://doi.org/10.1007/978-3-540-73888-6_45
Publication status	Published - 2006

Publication series

Name	Lecture Notes in Computer Science
Volume	4518
ISSN (Print)	0302-9743

Access to Document

10.1007/978-3-540-73888-6_45

Cite this

Knijf, de, J. (2006). FAT-CAT : frequent attributes tree based classification. In N. Fuhr, M. Lalmas, & A. Trotman (Eds.), Comparative Evaluation of XML Information Retrieval Systems (5th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2006, Dagstuhl Castle, Germany, December 17-20, 2006, Revised and Selected Papers) (pp. 485-496). (Lecture Notes in Computer Science; Vol. 4518). Springer. https://doi.org/10.1007/978-3-540-73888-6_45

Knijf, de, J. / FAT-CAT : frequent attributes tree based classification. Comparative Evaluation of XML Information Retrieval Systems (5th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2006, Dagstuhl Castle, Germany, December 17-20, 2006, Revised and Selected Papers). editor / N. Fuhr ; M. Lalmas ; A. Trotman. Berlin : Springer, 2006. pp. 485-496 (Lecture Notes in Computer Science).

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abstract = "The natural representation of XML data is to use the underlying tree structure of the data. When analyzing these trees we are ensured that no structural information is lost. These tree structures can be efficiently analyzed due to the existence of frequent pattern mining algorithms that works directly on tree structured data. In this work we describe a classification method for XML data based on frequent attribute trees. From these frequent patterns we select so called emerging patterns, and use these as binary features in a decision tree algorithm. The experimental results show that combining emerging attribute tree patterns with standard classification methods, is a promising combination to tackle the classification of XML documents.",

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Knijf, de, J 2006, FAT-CAT : frequent attributes tree based classification. in N Fuhr, M Lalmas & A Trotman (eds), Comparative Evaluation of XML Information Retrieval Systems (5th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2006, Dagstuhl Castle, Germany, December 17-20, 2006, Revised and Selected Papers). Lecture Notes in Computer Science, vol. 4518, Springer, Berlin, pp. 485-496. https://doi.org/10.1007/978-3-540-73888-6_45

FAT-CAT : frequent attributes tree based classification. / Knijf, de, J.
Comparative Evaluation of XML Information Retrieval Systems (5th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2006, Dagstuhl Castle, Germany, December 17-20, 2006, Revised and Selected Papers). ed. / N. Fuhr; M. Lalmas; A. Trotman. Berlin: Springer, 2006. p. 485-496 (Lecture Notes in Computer Science; Vol. 4518).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AB - The natural representation of XML data is to use the underlying tree structure of the data. When analyzing these trees we are ensured that no structural information is lost. These tree structures can be efficiently analyzed due to the existence of frequent pattern mining algorithms that works directly on tree structured data. In this work we describe a classification method for XML data based on frequent attribute trees. From these frequent patterns we select so called emerging patterns, and use these as binary features in a decision tree algorithm. The experimental results show that combining emerging attribute tree patterns with standard classification methods, is a promising combination to tackle the classification of XML documents.

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Knijf, de J. FAT-CAT : frequent attributes tree based classification. In Fuhr N, Lalmas M, Trotman A, editors, Comparative Evaluation of XML Information Retrieval Systems (5th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2006, Dagstuhl Castle, Germany, December 17-20, 2006, Revised and Selected Papers). Berlin: Springer. 2006. p. 485-496. (Lecture Notes in Computer Science). doi: 10.1007/978-3-540-73888-6_45

FAT-CAT : frequent attributes tree based classification

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