Study of Association Rule Mining and Different Hiding Techniques

Abstract

Data mining is the process of extracting hidden patterns from data. As more data is gathered,with the amount of data doubling every three years, data mining is becoming an increasingly important tool to transform this data into information. In this paper, we first focused on
APRIORI algorithm, a popular data mining technique and compared the performances of a linked list based implementation as a basis and a tries-based implementation on it for mining frequent item sequences in a transactional database. We examined the data structure,
implementation and algorithmic features mainly focusing on those that also arise in frequent item set mining. This algorithm has given us new capabilities to identify associations in large data sets. But a key problem, and still not sufficiently investigated, is the need to balance the confidentiality of the disclosed data with the legitimate needs of the data users. One rule is
characterized as sensitive if its disclosure risk is above a certain privacy threshold. Sometimes, sensitive rules should not be disclosed to the public, since among other things, they may be used for inferring sensitive data, or they may provide business competitors with an advantage. So, next we worked with some association rule hiding algorithms and examined their performances in order to analyze their time complexity and the impact that they have in the original database.
We worked on two different side effects – one was the number of new rules generated during the hiding process and the other one was the number of non-sensitive rules lost during the process.