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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(3): 274-277 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.129

Exploiting Hierarchal Structure of XML Data Using Association Rule Analysis

Gurpreet Kaur and Naveen Aggarwal

Abstract—Data mining is the process of extracting useful information from the huge amount of data stored in the databases. Data mining tools and techniques help to predict business trends those can occur in near future. Association rule mining is an important technique to discover hidden relationships among items in the transaction. Association rules is a popular and well researched method for finding interesting relation between variables in large databases. For generating strong association rules, it depends on the association rule extraction by any algorithm for example Apriori algorithm or FP-growth etc and the evolution of the rules by different interestingness measure for example support/confidence, lift/interest, Correlation Coefficient, Statistical Correlation, Leverage, Conviction etc. The classical model of association rules mining is support-confidence. The goal is to experimentally evaluate association rule mining approaches in the context of XML databases. Algorithms are implemented using Java. For experimental evaluation different XML datasets are used. Apriori and FP Tree algorithm have been implemented and their performance is evaluated extensively.

Index Terms—Data mining, association rule analysis, XML.

Gurpreet Kaur is with the Chandigarh Group of Colleges, Gharuan, Mohali, India (email:gurpreetkaur7885@gmail.com).
Naveen Aggarwal is with the UIET Punjab University, Chandigarh, India (email: navagg@gmail.com).

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Cite: Gurpreet Kaur and Naveen Aggarwal, "Exploiting Hierarchal Structure of XML Data Using Association Rule Analysis," International Journal of Machine Learning and Computing vol. 2, no. 3, pp. 274-277, 2012.

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