New method about how to construct decision tree based on association rule

2011 IEEE International Workshop on Open-source Software for Scientific Computation Pub Date : 2011-10-01 DOI:10.1109/OSSC.2011.6184708

Jing Gao, Baoyong Zhao

引用次数: 2

Abstract

Nowadays, decision tree is widely used as one of the most powerful tools in data mining. However, to construct an optimization decision tree is a complete NP problem. So a new method about how to construct decision tree, which is based on association rule mining, is proposed in this paper. Firstly, approximate exact rule with high reliability is defined. Secondly new attributes are generated from the approximate exact rule. And then its evaluation method is discussed in detail. Thirdly, the decision tree is constructed with both the new generated attributes and its original data. Finally, after comprehensive analysis, experimental results show that this new method has higher accuracy than any other old method.

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基于关联规则构造决策树的新方法

决策树作为数据挖掘中最强大的工具之一得到了广泛的应用。然而，构造最优化决策树是一个完全的NP问题。为此，本文提出了一种基于关联规则挖掘的决策树构造方法。首先，定义了具有高可靠性的近似精确规则。其次，根据近似精确规则生成新的属性。然后详细讨论了其评价方法。第三，利用新生成的属性和原始数据构建决策树。最后，经过综合分析，实验结果表明，该方法比其他旧方法具有更高的精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2011 IEEE International Workshop on Open-source Software for Scientific Computation

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