Methodology for Hiding Sensitive Information and Pruning Infrequent Itemsets for Association Rule Mining

Abstract

Association Rule Mining plays a major role in current research. This classical algorithm extracts frequent itemsets from large dataset which identifies Correlation between different items in the Transaction. Main issue in this algorithm is doubling the data scanning time. Many algorithms are proposed to find association rule and avoid complexity. This paper highlights two algorithms such as Novel Pruning approach for association rule mining and Hiding of Sensitive Association Rule by using improved Apriori algorithm. Finally, Suggested an integrated approach for Filtering Infrequent Itemsets and hiding Sensitive Association Rules using Same method which removes infrequent itemsets for hiding sensitive items in the Dataset.