An algorithm to mine general association rules from tabular data

Most methods for mining association rules from tabular data mine simple rules which only use the equality operator “=” in their items. For quantitative attributes, approaches tend to discretize domain values by partitioning them into intervals. Limiting the operator only to “=” results in many interesting frequent patterns that may not be identified. It is obvious that where there is an order between objects, operators such as greater than or less than a given value are as important as the equality operator. This motivates us to extend association rules, from the simple equality operator, to a more general set of operators. We address the problem of mining general association rules in tabular data where rules can have all operators {⩽, >, ≠, =} in their antecedent part. The proposed algorithm, mining general rules (MGR), is applicable to datasets with discrete-ordered attributes and on quantitative discretized attributes. The proposed algorithm stores candidate general itemsets in a tree structure in such a way that supports of complex itemsets can be recursively computed from supports of simpler itemsets. The algorithm is shown to have benefits in terms of time complexity, memory management and has good potential for parallelization.