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首页> 外文期刊>International Journal of Production Research >Association rule-generation algorithm for mining automotive warranty data
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Association rule-generation algorithm for mining automotive warranty data

机译：关联规则生成算法，用于挖掘汽车保修数据

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摘要

A new association rule-generation algorithm is presented for mining automotive warranty data. The algorithm uses elementary set concept and database manipulation techniques to develop useful relationships between product attributes and causes of failure. These relationships (knowledge) are represented using IF-THEN association rules, where the IF portion of the rule includes set of attributes representing product features (e.g. production date, repair date, mileage-at-repair, transmission, engine type, etc.) and the THEN portion of the rule includes set of attributes that represent decision outcome (e.g. problem-related labor code). Once association rules are developed, the algorithm applies a statistical analysis technique to evaluate the significance of each rule. The rules that pass the significance test are reported in a solution. Application of the association rule-generation algorithm is presented with a data-mining case study from the automotive industry. The knowledge (rules) extracted from the automotive warranty data are used to identify root causes of a particular warranty problem or to develop useful conclusions. Detailed discussion on the source and characteristics of warranty data is also presented.
机译：提出了一种新的关联规则生成算法，用于挖掘汽车保修数据。该算法使用基本集概念和数据库处理技术来开发产品属性和故障原因之间的有用关系。这些关系（知识）使用IF-THEN关联规则表示，其中规则的IF部分包括代表产品特征的属性集（例如，生产日期，维修日期，维修里程，变速器，发动机类型等）。规则的THEN部分包括代表决策结果的属性集（例如，与问题相关的劳动法规）。一旦建立了关联规则，该算法就会应用统计分析技术来评估每个规则的重要性。通过重要性测试的规则在解决方案中报告。结合汽车行业的数据挖掘案例研究，介绍了关联规则生成算法的应用。从汽车保修数据中提取的知识（规则）用于识别特定保修问题的根本原因或得出有用的结论。还提供了有关保修数据来源和特征的详细讨论。

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