Confabulation-inspired association rule mining for rare and frequent itemsets

A new confabulation-inspired association rule mining (CARM) algorithm is proposed using an interestingness measure inspired by cogency. Cogency is only computed based on pairwise item conditional probability, so the proposed algorithm mines association rules by only one pass through the file. The proposed algorithm is also more efficient for dealing with infrequent items due to its cogency-inspired approach. The problem of associative classification is used here for evaluating the proposed algorithm. We evaluate CARM over both synthetic and real benchmark data sets obtained from the UC Irvine machine learning repository. Experiments show that the proposed algorithm is consistently faster due to its one time file access and consumes less memory space than the Conditional Frequent Patterns growth algorithm. In addition, statistical analysis reveals the superiority of the approach for classifying minority classes in unbalanced data sets.