@inproceedings{cb5ab2605e8b45f09acd63f64870c2af,
title = "Discovering associations in XML data",
abstract = "Knowledge inference from semi-structured data can utilize frequent sub structures, in addition to frequency of data items. In fact, the working assumption of the present study is that frequent sub-trees of XML data represent sets of tags (objects) that are meaningfully associated. A method for extracting frequent sub-trees from XML data is presented. It uses thresholds on frequencies of paths and on the multiplicity of paths in the data. The frequent sub-trees are extracted and counted in a procedure that has O(n2) complexity. The data content of the extracted sub-trees, in the form of attribute values, is cast in tabular form. This enables a search for associations in the extracted data. Thus, the complete procedure uses structure and content to extract association rules from semistructured data. A large industrial example is used to demonstrate the operation of the proposed method.",
author = "A. Meisels and M. Orlov and T. Maor",
note = "Publisher Copyright: {\textcopyright} 2002 IEEE.; 3rd International Conference on Web Information Systems Engineering Workshops, WISE 2002 ; Conference date: 11-12-2002",
year = "2002",
month = jan,
day = "1",
doi = "10.1109/WISEW.2002.1177861",
language = "English",
series = "WISE 2002 - Proceedings of the 3rd International Conference on Web Information Systems Engineering Workshops",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "178--183",
editor = "Bo Huang and Ling, \{Tok Wang\} and Mukesh Mohania and Ng, \{Wee Keong\} and Ji-Rong Wen and S.K. Gupta",
booktitle = "WISE 2002 - Proceedings of the 3rd International Conference on Web Information Systems Engineering Workshops",
address = "United States",
}