A Novel Association Rule Mining Algorithm to Enhance Confidentiality in Data Mining

Abstract

Data mining is the process of extracting hidden patterns from data. As more data is gathered, with the amount of data increasing every year, data mining is becoming an increasingly important tool to transform this data into information. We focus on APRIORI algorithm, a popular data mining technique and analyze the performance of linked list based implementation as a basis for mining frequent item sequences in a transactional database. This algorithm has given us new capabilities to identify associations in large data sets. But an important issue, still not sufficiently scanned, is the need to balance the confidentiality of the disclosed data with the legitimate needs of the data users. We work with some association rule hiding algorithms and examine their performances in order to analyze their time complexity and the impact that they have in the original database. We work a side effect – the number of new rules generated during the hiding process.