An XML-Enabled Association Rule Framework
With the sheer amount of data stored, presented and exchanged using XML nowadays, the ability to extract knowledge from XML data sources becomes increasingly important and desirable. This paper aims to integrate the newly emerging XML technology with data mining technology, using association rule mining as a case in point. Compared with traditional association mining in the well-structured world (e.g., relational databases), mining from XML data is faced with more challenges due to the inherent flexibilities of XML in both structure and semantics. The primary challenges include 1) a more complicated hierarchical data structure; 2) an ordered data context; and 3) a much bigger data size. To tackle these challenges, in this paper, we propose an extended XML-enabled association rule framework, which is flexible and powerful enough to represent both simple and complex structured association relationships inherent in XML data.
KeywordsAssociation rule semi-structure XML
Unable to display preview. Download preview PDF.
- 1.Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proc. of the ACM SIGMOD Intl. Conf. on Management of Data, Washington D.C., USA, pp. 207–216 (May 1993)Google Scholar
- 2.Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. of the 20th Intl. Conf. on Very Large Data Bases, Santiago, Chile, pp. 478–499 (September 1994)Google Scholar
- 3.Han, J., Fu, Y.: Discovery of multiple-level association rules from large databases. In: Proc. of the 21st Intl. Conf. on Very Large Data Bases, Zurich, Switzerland, pp. 420–431 (September 1995)Google Scholar
- 4.Park, J.-S., Chen, M.-S., Yu, P.S.: An effective hash based algorithm for mining association rules. In: Proc. of the ACM SIGMOD Intl. Conf. on Management of Data, San Jose, CA, pp. 175–186 (May 1995)Google Scholar
- 5.Shapiro, S.C.: Cables, paths and subconscious reasoning in propositional semantic networks (chapter). In: Sowa, J.F. (ed.) Principles of Semantic Networks – Explorations in the Representation of Knowledge (1991)Google Scholar
- 6.Srikant, R., Agrawal, R.: Mining generalized association rules. In: Proc. of the 21st Intl. Conf. on Very Large Data Bases, Zurich, Switzerland, pp. 409–419 (September 1995)Google Scholar
- 7.Toivonen, H.: Sampling large databases for association rules. In: Proc. of the 22th Conference on Very Large Data Bases, Mumbai, India, pp. 134–145 (September 1996)Google Scholar
- 8.Wang, K., He, Y., Han, J.: Mining frequent itemsets using support constraints. In: Proc. 26st Intl. Conf. Very Large Data Bases, Cairo, Egypt, pp. 43–52 (September 2000)Google Scholar