Sensitivity Association Rule Mining using Weight based Fuzzy Logic

Abstract

Mining of sensitive rules is the most important task in data mining. Most of the existing techniques worked on finding sensitive rules based upon the crisp thresh hold value of support and confidence which cause serious side effects to the original database. To avoid these crisp boundaries this paper aims to use WFPPM (Weighted Fuzzy Privacy Preserving Mining) to extract sensitive association rules. WFPPM completely find the sensitive rules by calculating the weights of the rules. At first, we apply FP-Growth to mine association rules from the database. Next, we implement fuzzy to find the sensitive rules among the extracted rules. Experimental results show that the proposed scheme find actual sensitive rules without any modification along with maintaining the quality of the released data as compared to the previous techniques.

An Optimization based Algorithm for Association Rule Mining in Large Databases

“…The experimental results revealed that this approach extracted sensitive rules by taking into account of the weight of every single parameter instead of depending up on the minimal threshold value of the confidence as well as support. Also this approach had the capability to implement in various applications such as bank and other financial sectors [16].…”