Privacy Preserving in Data Mining using FP Growth Algorithm on Hybrid Partitioned Dataset

Data mining is used in various business domains to extract important information from the large data repositories. In this paper, Horizontal and Vertical data distribution is combined to provide privacy to the data. FP Growth algorithm on hybrid partitioned dataset is used to decrease the execution time for generation of rules. The experiments are carried out on the two datasets namely adult and credit dataset and results are predicted on the basis of Apriori and FP Growth algorithm. The experimental results show that the FP Growth algorithm is better in performance than Apriori algorithm in terms of execution time because FP Growth algorithm takes less time to generate rules.