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A Novel Association Rule Prediction Algorithm for Classification and Regression

机译：分类和回归的一种新的关联规则预测算法

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摘要

To avoid generating a large number of candidate itemsets during the association rules mining and improve the prediction performance, a new association rule prediction algorithm for classification and regression (ARPACR) is proposed according to the advantages of matrix operation and tree structure. Firstly, the association rules are mined by constructing a new frequent tree. Then, the consequents of the association rules are reconstructed to achieve the classification and regression prediction for new sample. Finally, the experiment results show that it is competitive in prediction accuracy and mining efficiency by comparing with the other algorithms.
机译：为了避免在关联规则挖掘过程中产生大量候选项集并提高预测性能，针对矩阵运算和树结构的优点，提出了一种新的分类回归关联规则预测算法（ARPACR）。首先，通过构造新的频繁树来挖掘关联规则。然后，重构关联规则的结果，以实现对新样本的分类和回归预测。最后，实验结果表明，与其他算法相比，该算法在预测精度和挖掘效率上具有竞争力。

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