The sharing of association rules is often beneficial in industry, but requires privacy safeguards. One may decide to disclose only part of the knowledge and conceal strategic patterns which we call restrictive rules. These restrictive rules must be protected before sharing since they are paramount for strategic decisions and need to remain private. To address this challenging problem, we propos...

This paper proposes an algorithm to discover novel association rules, combined association rules. Compared with conventional association rule, this combined association rule allows users to perform actions directly. Combined association rules are always organized as rule sets, each of which is composed of a number of single combined association rules. These single rules consist of non-actionabl...

Data mining is an iterative and interactive process that explores and analyzes voluminous digital data to discover valid, novel, and meaningful patterns (Mohammed, 1999). Since digital data may have terabytes of records, data mining techniques aim to find patterns using computationally efficient techniques. It is related to a subarea of statistics called exploratory data analysis. During the pa...

Association rule algorithms typically only identify patterns that occur in the original form throughout the database. In databases which contain many small variations in the data, potentially important discoveries may be ignored as a result. In this paper, we describe an associate rule mining algorithm that searches for approximate association rules. Our ~AR approach allows data that approximat...

This paper introduces knowledgebase approximation and fusion using association rule aggregation as a means to facilitate accelerated insight induction from high-dimensional disparate knowledgebases. There are two typical observations that make approximating knowledgebases of interest: (1) It is quite often insights can be derived based partial set the samples, not necessarily all them; (2) gene...