A Classification Algorithm Based on an Association Rule of Multiple Frequent Item-Sets

Zhiheng Liang
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Abstract

It is necessary to discrete datasets firstly if you want to data mining an association rule of datasets consisting of many categorical and numeric attributes by a traditional algorithm. However, in view of the versatility, the applications of the traditional algorithm are limited. This paper propose a new algorithm called ARMFI(Association Rule of Multiple Frequent Item-sets) which can data mining an Association Rule from datasets consisting of many categorical and numeric attributes directly and completely, and overcome disadvantage of the traditional algorithm. The result has been proofed that the ARMFI shows better performances than the traditional algorithm¿
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基于多频繁项集关联规则的分类算法
如果要用传统的算法对由许多分类属性和数值属性组成的数据集进行关联规则挖掘,首先需要对离散数据集进行挖掘。然而,由于其通用性,传统算法的应用受到了限制。本文提出了一种新的算法ARMFI(Multiple frequency item -set Association Rule of Multiple frequency item -set),该算法可以直接完整地从由许多分类属性和数字属性组成的数据集中挖掘出关联规则,克服了传统算法的缺点。实验结果表明,该算法比传统算法具有更好的性能
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