Mining Association Rules from XML Data

作者: Daniele Braga , Alessandro Campi , Mika Klemettinen , PierLuca Lanzi

DOI: 10.1007/3-540-46145-0_3

关键词:

摘要: The eXtensible Markup Language (XML) rapidly emerged as a standard for representing and exchanging information. fastgrowing amount of available XML data sets pressing need languages tools to manage collections documents, well mine interesting information out them. Although the mining community has not yet rushed into use XML, there have been some proposals exploit XML. However, in practice these mainly rely on more or less traditional relational databases with an interface. In this paper, we introduce association rules from native documents discuss new challenges opportunities that topic community. More specifically, extension XQuery rules. This is used throughout paper better define rule within emphasize its implications context.

参考文章(17)
Heikki Mannila, A. Inkeri Verkamo, Hannu Toivonen, Efficient algorithms for discovering association rules knowledge discovery and data mining. pp. 181- 192 ,(1994)
Tomasz Imieliński, Aashu Virmani, MSQL: A Query Language for Database Mining Data Mining and Knowledge Discovery. ,vol. 3, pp. 373- 408 ,(1999) , 10.1023/A:1009816913055
Ramakrishnan Srikant, Rakesh Agrawal, Mining Generalized Association Rules very large data bases. pp. 407- 419 ,(1995)
Helena Ahonen, Oskari Heinonen, Mika Klemettinen, A. Inkeri Verkamo, Mining in the Phrasal Frontier european conference on principles of data mining and knowledge discovery. pp. 343- 350 ,(1997) , 10.1007/3-540-63223-9_133
Giuseppe Psaila, Stefano Ceri, Rosa Meo, A New SQL-like Operator for Mining Association Rules very large data bases. ,vol. 22, pp. 122- 133 ,(1996)
Jiawei Han, Yongjian Fu, Discovery of Multiple-Level Association Rules from Large Databases very large data bases. pp. 420- 431 ,(1995)
M. Rajman, R. Besançon, Text Mining: Natural Language techniques and Text Mining applications Data Mining and Reverse Engineering. pp. 50- 64 ,(1998) , 10.1007/978-0-387-35300-5_3
R. Meo, G. Psaila, S. Ceri, A tightly-coupled architecture for data mining international conference on data engineering. ,vol. 14, pp. 316- 323 ,(1998) , 10.1109/ICDE.1998.655794
Ramakrishnan Srikant, Rakesh Agrawal, Mining quantitative association rules in large relational tables international conference on management of data. ,vol. 25, pp. 1- 12 ,(1996) , 10.1145/233269.233311
Lisa Singh, Peter Scheuermann, Bin Chen, Generating association rules from semi-structured documents using an extended concept hierarchy conference on information and knowledge management. pp. 193- 200 ,(1997) , 10.1145/266714.266895