Mining Association rules and Differential Privacy Preservation using Randomization - Tripathi, Krishna Kumar; Narendra S, Chaudhari (International Journal of Computer Sciences and Engineering, 2025)
Paper herewith proposes an optimal predictive class association rule mining techniques for extracting the minimum rule having same predictive power of complete predictive class association rule by using predictive association rule set instead of complete class association rule , proposed methodologies in this paper can avoid the redundant and non-useful computation that would otherwise be required or needed for the mining of predictive class association rules and therefore improving the efficiency and effectiveness of the mining process significantly. Paper herewith presents an efficient and effective algorithm framework for mining the optimal predictive class association rule dataset by using CPAR before they are actually generated. In this paper, techniques have been implemented and obtained experimental results demonstrate that the algorithm generates the optimal class association rule set. Hence paper herewith propose a new data classification approach, Classification based on the Predictive Association Rules, which mainly combines the advantages and knowledge of both traditional rule-based and associative classification. Instead of generating the large number of candidate class association rules as in associative classification techniques, CPAR usually adopts a greedy algorithm for generating rules directly from the training dataset.