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An optimization of association rule mining for large database using K- map and Genetic Algorithm: A review

机译:基于K-map和遗传算法的大型数据库关联规则挖掘优化:综述

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In the field of data mining association rule is a very popular and efficient technique while it has different technique such as classification, clustering, sequential pattern etc to extract or optimize the large database. The main aim of data mining is to find an effective or an optimized set of data from the big database. An association rulesmining technique is used in various applications such as in banking, department stores etc. The Genetic Algorithm (GA) system can expect the rules which include negative attributes in the created rules mutually more than one attribute in resulting part. In this paper, we explore a review to optimize association rules using K- map and genetic algorithm (GA).
机译:在数据挖掘领域,关联规则是一种非常流行且有效的技术,它具有诸如分类,聚类,顺序模式等不同的技术来提取或优化大型数据库。数据挖掘的主要目的是从大型数据库中找到有效或优化的数据集。关联规则挖掘技术已在银行,百货公司等各种应用中使用。遗传算法(GA)系统可以期望所创建规则中包含负属性的规则比结果部分中的一个属性多。在本文中,我们探索了使用K-map和遗传算法(GA)来优化关联规则的评论。

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