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首页> 外文期刊>Journal of information and computational science >A High Risk Association Rule Mining Algorithm for the Petroleum Industry
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A High Risk Association Rule Mining Algorithm for the Petroleum Industry

机译：石油行业的高风险关联规则挖掘算法

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摘要

Association rule mining has become a significant issue in recent years. Most existing works focus on transaction database and high utility data mining. However, the association rule mining technologies are rarely used in risk analysis. In this paper, to mine interesting information from real hidden danger data in the petroleum industry, we define a high risk association rule mining problem and propose an algorithm to it. First, the hidden danger data are represented by a Hidden Danger Occurrence Table (HDOT) and a risk weight table. Then, we define a high risk association rule mining problem. The purpose is to find all high risk association rules in the HDOT. Finally, we propose a high risk association rule mining algorithm to this problem. It is similar to an existing high utility rule mining approach, however more appropriate for this problem. It has three parameters including minimum confidence, minimum support and high risk threshold. The output is the set of all high risk association rules. According to users' evaluation, some rules are common-sense, while others are interesting.
机译：关联规则挖掘已成为近年来的重要问题。现有的大多数工作都集中在事务数据库和高效数据挖掘上。但是，关联规则挖掘技术很少用于风险分析中。在本文中，为了从石油行业的真实隐患数据中挖掘出有趣的信息，我们定义了一个高风险关联规则挖掘问题，并提出了一种算法。首先，隐患数据由隐患发生表（HDOT）和风险权重表表示。然后，我们定义了一个高风险关联规则挖掘问题。目的是在HDOT中找到所有高风险关联规则。最后，针对该问题提出了一种高风险关联规则挖掘算法。它类似于现有的高实用性规则挖掘方法，但是更适合此问题。它具有三个参数，包括最小置信度，最小支持和高风险阈值。输出是所有高风险关联规则的集合。根据用户的评估，有些规则是常识，而另一些则很有趣。

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