IEEE Trans Neural Netw Learn Syst. 2014 Nov;25(11):2053-64. doi: 10.1109/TNNLS.2014.2303137.
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.
提出了一种新的基于虚构联想的关联规则挖掘（CARM）算法，该算法使用了一种基于一致性的有趣性度量。一致性仅基于项对的条件概率进行计算，因此该算法仅通过一次文件遍历即可挖掘关联规则。由于采用了一致性启发式方法，该算法在处理罕见项目时也更加高效。关联分类问题用于评估所提出的算法。我们在从 UCI 机器学习存储库获得的合成和真实基准数据集上评估了 CARM。实验表明，由于其一次文件访问，该算法始终更快，并且比条件频繁模式增长算法消耗更少的内存空间。此外，统计分析表明，该方法在不平衡数据集中小数类别的分类中具有优越性。
