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Aided analysis for quality function deployment with an Apriori-based data mining approach

机译：使用基于Apriori的数据挖掘方法进行质量功能部署的辅助分析

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摘要

Quality function deployment (QFD) is a proven useful methodology in new product development to satisfy customer requirements (CRs). House of quality (HoQ), the general implementing mode of QFD, is aimed to identify the variables of engineering characteristics (ECs) based on the relationships between CRs and ECs. Traditionally, the establishment of these relationships is mainly dependent on the designers' experience and then the HoQ included many items difficult to handle. For aiding the designers on the HoQ analysis, the paper proposes an Apriori-based data mining approach to extract knowledge from historical data. The approach is mainly focused on mining potential useful association rules (including positive and negative rules) that reflect the relationships according to three objectives: support, confidence, and interestingness. For ensuring the availability and conciseness of these extracted rules, the definitions and calculations of rule conflict and redundancy are proposed and processing procedures are also developed to unite or delete unnecessary rules. The reserved rules are clustered in order to facilitate rule management and reuse. Furthermore, a reuse procedure is also developed for new HoQ analysis. Computational experiments of an electrically powered bicycle are used to illustrate the proposed approach and its capability of extracting useful knowledge.
机译：质量功能部署（QFD）是在新产品开发中满足客户需求（CR）的一种行之有效的方法。质量屋（HoQ）是QFD的普遍实施模式，旨在根据CR与EC之间的关系来识别工程特征（EC）变量。传统上，这些关系的建立主要取决于设计师的经验，然后HoQ包含许多难以处理的项目。为了帮助设计人员进行HoQ分析，本文提出了一种基于Apriori的数据挖掘方法来从历史数据中提取知识。该方法主要侧重于挖掘潜在的有用关联规则（包括肯定和否定规则），这些规则根据三个目标来反映关系：支持，信心和兴趣。为了确保所提取规则的可用性和简洁性，提出了规则冲突和冗余的定义和计算方法，并且开发了处理程序以合并或删除不必要的规则。保留的规则被聚类，以便于规则管理和重用。此外，还为新的HoQ分析开发了重用过程。电动自行车的计算实验被用来说明所提出的方法及其提取有用知识的能力。

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