Mining high coherent association rules with consideration of support measure

• We propose a HCAR algorithm for mining high coherent association rules.
• The derived rules have the logical equivalence property.
• The derived rules are expected to be more reliable in terms of business.
• Lower and upper bounds of itemsets are defined for speeding up the process.
• Two datasets are used to show the proposed approach is effective.

Data mining has been studied for a long time. Its goal is to help market managers find relationships among items from large databases and thus increase sales volume. Association-rule mining is one of the well known and commonly used techniques for this purpose. The Apriori algorithm is an important method for such a task. Based on the Apriori algorithm, lots of mining approaches have been proposed for diverse applications. Many of these data mining approaches focus on positive association rules such as “if milk is bought, then cookies are bought”. Such rules may, however, be misleading since there may be customers that buy milk and not buy cookies. This paper thus takes the properties of propositional logic into consideration and proposes an algorithm for mining highly coherent rules. The derived association rules are expected to be more meanful and reliable for business. Experiments on two datasets are also made to show the performance of the proposed approach.