An XML-Enabled Association Rule Framework

L. Feng, T. Dillon, H. Weigand, E. Chang

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

36 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationDatabase and Expert Systems Applications
Subtitle of host publication14th International Conference, DEXA 2003, Prague, Czech Republic, September 1-5, 2003. Proceedings
EditorsVladimir Marik, Werner Retschitzegger, Olga Stepankova
Number of pages10
ISBN (Electronic)978-3-540-45227-0
Publication statusPublished - Sep 2003
Event14th International Conference on Database and Expert Systems Applications, DEXA 2003 - Prague, Czech Republic
Duration: 1 Sep 20035 Sep 2003
Conference number: 14

Publication series

NameLecture notes in computer science


Conference14th International Conference on Database and Expert Systems Applications, DEXA 2003
Abbreviated titleDEXA
Country/TerritoryCzech Republic


  • IR-63502
  • EWI-7214


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