Abstract
With the sheer amount of data stored, presented and exchanged using XML nowadays, the ability to extract interesting knowledge from XML data sources becomes increasingly important and desirable. In support of this trend, several encouraging attempts at developing methods for mining XML data have been proposed. However, efficiency and simplicity are still barrier for further development. In this paper, we show that any XML document can be mined for association rules using only a specially devised hierarchical data structure called HoPS without multiple XML data scans. It is flexible and powerful enough to represent both simple and complex structured association relationships inherent in XML data.
This work was supported in part by Ubiquitous computing Technology Research Institute and by the University IT Research Center Project, funded by the Korean Ministry of Information and Communication.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Proc. of the ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. of the 20th International Conference on Very Large Data Bases, pp. 478–499 (1994)
Braga, D., Campi, A., Klemettinen, M., Lanzi, P.L.: Mining Association Rules from XML Data. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2002. LNCS, vol. 2454, pp. 21–30. Springer, Heidelberg (2002)
Braga, D., Campi, A., Ceri, S., Klemettinen, M., Lanzi, P.L.: A tool for extracting XML association rules. In: Proc. of the 14th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2002), pp. 57–64 (2002)
Han, J., Fu, Y.: Discovery of multiple-level association rules from large databases. In: Proc. of the 21st International Conference on Very Large Data Bases, pp. 420–431 (1995)
Meo, R., Pasila, G., Ceri, S.: An extension to SQL for mining association rules. Data Mining and Knowledge Discovery 2(2), 195–224 (1998)
Singh, L., Scheuermann, P., Chen, B.: Generating association rules from semistructureddocuments using an extended concept hierarchy. In: Proc. of the 6th International Conference on Information and Knowledge Management (CIKM 1997), pp. 193–200 (1997)
Srikant, R., Agrawal, R.: Mining generalized association rules. In: Proc. of the 21st International Conference on Very Large Data Bases, pp. 409–419 (1995)
Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. In: Proc. of the 1996 ACM SIGMOD International Conference on Management of Data, pp. 1–12 (1996)
Toivonen, H.: Sampling large databases for association rules. In: Proc. of the 22th International Conference on Very Large Data Bases, pp. 43–52 (1996)
Wan, J.W.W., Dobbie, G.: Extracting association rules from XML documents using XQuery. In: Proc. of the 5th ACM International Workshop on Web Information and Data Management (WIDM 2003), pp. 94–97 (2003)
The World Wide Web Consortium (W3C). Extensible Markup Language (XML) 1.0 (Third Edition) W3C Recommendation (2004), http://www.w3.org/TR/2004/RECxml-20040204/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Paik, J., Youn, H.Y., Kim, U. (2005). A New Method for Mining Association Rules from a Collection of XML Documents. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424826_101
Download citation
DOI: https://doi.org/10.1007/11424826_101
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25861-2
Online ISBN: 978-3-540-32044-9
eBook Packages: Computer ScienceComputer Science (R0)