一种高相干关联规则挖掘算法

Chun-Hao Chen, Guo-Cheng Lan, T. Hong, Yui-Kai Lin
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引用次数: 3

摘要

数据挖掘的目标是帮助市场经理从大型数据集中发现产品之间的关系,从而增加销售量。Apriori算法是一种关联规则挖掘方法,是一种数据挖掘技术。尽管基于Apriori算法已经提出了很多挖掘方法,但大多数都集中在积极的关联规则上,例如“如果买了牛奶,那么就买了面包”。然而,这样的规定可能会产生误导,因为购买牛奶的顾客可能不会购买面包。本文提出了一种考虑命题逻辑特性的高相干规则挖掘算法。因此,派生的关联规则可能更加周到和可靠。在仿真数据集上进行了实验,验证了该方法的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A High Coherent Association Rule Mining Algorithm
The goal of data mining is to help market managers find relationships among items from large data sets to increase sales volume. The Apriori algorithm is a method for association rule mining, a data mining technique. Although a lot of mining approaches have been proposed based on the Apriori algorithm, most focus on positive association rules, such as "If milk is bought, then bread is bought". However, such a rule may be misleading since customers that buy milk may not buy bread. In this paper, an algorithm for mining highly coherent rules that takes the properties of propositional logic into consideration is proposed. The derived association rules may thus be more thoughtful and reliable. Experiments are conducted on simulation data sets to demonstrate the performance of the proposed approach.
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