Research Repository

Logic-Based Association Rule Mining in XML Documents

Liu, Hong-Cheu, Jamil, Hasan M and Zeleznikow, John (2006) Logic-Based Association Rule Mining in XML Documents. In: Advanced Web and Network Technologies, and Applications: APWeb 2006 International Workshops: XRA, IWSN, MEGA, and ICSE, Harbin, China, January 16-18, 2006. Proceedings. Shen, Heng Tao, Li, Jinbao, Li, Minglu, Ni, Jun and Wang, Wei, eds. Lecture Notes in Computer Science (3842). Springer, Berlin, Germany, pp. 97-106.

Full text for this resource is not available from the Research Repository.


In this paper, we propose a new framework, called XLogic- Miner, to mine association rules from XML data. We consider the generate-and-test and the frequent-pattern growth approaches. In XLogic-Miner, we propose an novel method to represent a frequent-pattern tree in an object-relational table and exploit a new join operator developed in the paper. The principal focus of this research is to demonstrate that association rule mining can be expressed in an extended datalog program and be able to mine XML data in a declarative way. We also consider some optimization and performance issues.

Item Type: Book Section
ISBN: 9783540311584 (print) 9783540324355 (online)
Uncontrolled Keywords: ResPubID11175, data mining, Web database system, eXtensible Markup Language, XML, knowledge discovery, Apriori algorithm, SQL system, DBMS-based mining process, database management system, XLogic-Miner, association rule mining, Logic-based database language
Subjects: Historical > RFCD Classification > 280000 Information, Computing and Communication Sciences
Current > FOR Classification > 0801 Artificial Intelligence and Image Processing
Current > FOR Classification > 0803 Computer Software
Current > FOR Classification > 0806 Information Systems
Historical > Faculty/School/Research Centre/Department > School of Management and Information Systems
Depositing User: VUIR
Date Deposited: 18 Sep 2013 06:28
Last Modified: 18 Sep 2013 06:28
ePrint Statistics: View download statistics for this item
Citations in Scopus: 3 - View on Scopus

Repository staff only

View Item View Item

Search Google Scholar