In the end, the effectiveness of association rules in an experiment.

最后, 在实验中测试了关联规则生成规则模式的有效性.

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Mining association rules is an important topic in the data mining research.

关联规则采掘是数据采掘中重要的研究课题.

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Traditional association rules mining is called boolean associaion rules mining.

传统的关联规则挖掘称为布尔型关联规则挖掘.

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Association rules in the smallest rule set are called key rules.

最小规则集中的规则称为基规则.

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The mining of association rules needs a great mount of data.

关联规则的挖掘需要大量的数据.

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Discovering frequent itemsets is a key problem in data mining association rules.

发现频繁项目集是关联规则数据开采中的关键问题.

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The data mining system can discover association rules and decision rules.

本数据挖掘系统使用增量算法发现关联规则和提取决策规则.

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According to value of interest measure, association rules are classified into positive and negative association rules.

利用兴趣度将关联规则分为正关联规则和负关联规则, 从而可以用算法挖掘带有负项的关联规则.

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This paper proposes an algorithm: meta - rule - guided mining multiple level spatial association rules based on progressive refinement.

提出了元规则指导下逐步求精的多层空间关联规则挖掘算法.

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In the mining process of association rules, the essential step is generating frequent item set.

在挖掘关联规则的过程中, 关键步骤是产生频繁项集.

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To mine approximate temporal association rules in negotiation, an approach based on clustering was advanced.

针对协商过程的时态关联具有近似性的特点, 提出了一种基于聚类的近似时态关联规则挖掘方法.

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In this paper , we a new algorithm for mining association rules including the quantities of items.

本文所提出的演算法,可以找寻出包含项目数量的关联规则.

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It is a basic request for every member to abide by the association rules.

遵守会规是对每个会员最基本的要求.

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First, concepts and theories of the data mining and algorithms of association rules are introduced.

首先介绍了数据挖掘的相关概念和理论以及关联规则相关算法.

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By effectively building concept lattice, this method generates association rules after finding frequent itemsets.

该方法通过构造概念格来发现频繁项集,进而生成关联规则.

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