An efficient incremental algorithm for frequent itemsets mining in distorted databases with granular computing

In order to preserve individual privacy, original data is distorted with the perturbation technique, and with the support reconstruction method, frequent itemsets can be mined from the distorted database. Due to this, mining process can be apart from being error-prone, expensively, in the dynamic update environment, more expensive in terms of time as compared to the original database. Some methods proposed try to solve this problem, but still not efficient. To improve so, this paper makes use of a method based on Granular Computing (GrC) in incremental mining, which is efficient and accuracy in support computation. The experiment results show the efficiency of our algorithm.