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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >ETARM: an efficient top-k association rule mining algorithm
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ETARM: an efficient top-k association rule mining algorithm

机译：etaM：高效的Top-K关联规则挖掘算法

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摘要

Mining association rules plays an important role in data mining and knowledge discovery since it can reveal strong associations between items in databases. Nevertheless, an important problem with traditional association rule mining methods is that they can generate a huge amount of association rules depending on how parameters are set. However, users are often only interested in finding the strongest rules, and do not want to go through a large amount of rules or wait for these rules to be generated. To address those needs, algorithms have been proposed to mine the top-k association rules in databases, where users can directly set a parameter k to obtain the k most frequent rules. However, a major issue with these techniques is that they remain very costly in terms of execution time and memory. To address this issue, this paper presents a novel algorithm named ETARM (Efficient Top-k Association Rule Miner) to efficiently find the complete set of top-k association rules. The proposed algorithm integrates two novel candidate pruning properties to more effectively reduce the search space. These properties are applied during the candidate selection process to identify items that should not be used to expand a rule based on its confidence, to reduce the number of candidates. An extensive experimental evaluation on six standard benchmark datasets show that the proposed approach outperforms the state-of-the-art TopKRules algorithm both in terms of runtime and memory usage.
机译：矿业协会规则在数据挖掘和知识发现中发挥着重要作用，因为它可以揭示数据库中的项目之间的强烈关联。尽管如此，传统关联规则挖掘方法的重要问题是，它们可以根据参数的设置方式生成大量的关联规则。但是，用户通常只对查找最强的规则感兴趣，并且不想通过大量规则或等待要生成的这些规则。为满足这些需求，已提出算法在数据库中挖掘Top-K关联规则，其中用户可以直接设置参数k以获得最常用的规则。然而，这些技术的主要问题是，在执行时间和内存方面，它们仍然非常昂贵。要解决此问题，本文提出了一种名为“eTARM（高效Top-K关联规则挖掘机）的新颖算法，以有效地查找完整的Top-K关联规则集。所提出的算法集成了两个新颖的候选修剪特性以更有效地减少搜索空间。在候选选择过程中应用这些属性以识别不应用于扩展规则的项目，以基于其置信度，以减少候选者的数量。在六个标准基准数据集上进行了广泛的实验评估，表明，该方法在运行时和内存使用方面均优于最先进的Topkrules算法。

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