PPDM Model dependent Bayesian Network for XML Association Rules Mining
Khalid Iqbal, Sohail Asghar, Simon Fong
Journal of Networking Technology, Vol. 2, No. 3, 2011.
DOI: 
Link: http://www.dline.info/jnt/fulltext/v2n3/2.pdf
Abstract:
In Association Rule Mining (ARM) rules are the core which determine the mining process and its effectiveness. In the ARM, a central issue is the sensitivity which is either ignored or not addressed by the researchers in data mining. It is important to avoid senstive information disclosure in ARM. We propose to use Bayesian Network which is dependent on PPDM model and can reliably hide away sensitive rules in ARM. Additionally we advoate that the XML domain of PPDM can be reinforced with the use of sensitivity. We document clearly the efficacy of PPDM model with empirical validity in the current study. We also expect that the future research will address more issues of ARM based on PPDM model