Confabulation-Inspired Association Rule Mining for Rare and Frequent Itemsets
Confabulation-Inspired Association Rule Mining for Rare and Frequent Itemsets
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DOI：
10.1109/tnnls.2014.2303137
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发表时间：
2014-06
影响因子：
10.4
通讯作者：
Azadeh Soltani;M. Akbarzadeh-Totonchi
中科院分区：
文献类型：
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作者：
Azadeh Soltani;M. Akbarzadeh-Totonchi
提出了一种基于可信度的关联规则挖掘算法。该算法只根据两两项目的条件概率计算关联度，因此只需要一次遍历文件就可以挖掘出关联规则。该算法也更有效地处理不频繁的项目，由于其一致性启发的方法。这里使用的问题的关联分类评估所提出的算法。我们在从加州大学欧文分校机器学习库获得的合成和真实的基准数据集上评估CARM。实验结果表明，该算法是一致的速度，由于其一次文件访问和消耗更少的内存空间比条件频繁模式增长算法。此外，统计分析揭示了该方法在不平衡数据集中对少数类进行分类的优越性。
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.