A rough set based data mining approach for house of quality analysis

Li, Jing Rong; Wang, Qing Hui

doi:10.1080/00207540802665907

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…Concepts descriptions and the results from early simulations are the main sources of data, while a lot of different techniques for data analysis are employed, with the classification through decision trees being the most frequently used (see Figure 5). Keivanpour and Ait Kadi ( 2018) and Li and Wang (2010) used historical design data to established target specifications thought respectively neural network and rough set theory and decision tree. A different approach was taken by Chowdhery and Bertoni (2018) mining the second-hand online purchase data to set targets for the new machine to be designed.…”

Section: Data-driven Design In the Other Stages Of Concept Developmentmentioning

confidence: 99%

Data-Driven Design in Concept Development: Systematic Review and Missed Opportunities

Bertoni

2020

Proc. Des. Soc.: Des. Conf.

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The paper presents a systematic literature review investigating definitions, uses, and application of data-driven design in the concept development process. The analysis shows a predominance of the use of text mining techniques on social media and online reviews to identify customers’ needs, not exploiting the opportunity granted by the increased accessibility of IoT in cyber-physical systems. The paper argues that such a gap limits the potential of capturing tacit customers’ needs and highlights the need to proactively plan and design for a transition toward data-driven design.

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Section: Data-driven Design In the Other Stages Of Concept Developmentmentioning

confidence: 99%

Data-Driven Design in Concept Development: Systematic Review and Missed Opportunities

Bertoni

2020

Proc. Des. Soc.: Des. Conf.

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“…In the study of Li and Wang (2010), the rough set based data mining method can only support mining classification rule but not association rule. In other words, the decision variables must be classified into several categories when using the method.…”

Section: International Journal Of Computer Integrated Manufacturing 679mentioning

confidence: 99%

“…And then some potential strong rules between CRs and these ECs may be lost. For example, the example in Li and Wang (2010) used two PSs (maximum speed and driving range) for classifying the product and then only the relationships between CRs and product category (this also can be seen as the relationships between CRs and the set of the two ECs) are elicited. The possible relationships between CRs and one EC are not identified, e.g.…”

Section: International Journal Of Computer Integrated Manufacturing 679mentioning

confidence: 99%

“…Data mining, which has been widely applied in engineering design, is used to extract unknown or potential hidden knowledge in a dataset (Chen et al 1996, Choudhary et al 2009). For HoQ, Li and Wang (2010) proposed a rough set based data mining approach to elicit a minimal rule set for establishing the relationships between CRs and product categories. In the method, the products must be classified into several categories based on the values of ECs and then this is seen as category rule extraction.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Cheng

Chu

2010

International Journal of Computer Integrated Manufacturing

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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.

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“…Rough set theory is essential to obtain the interrelationships between CNs and design specifications, on which the reduction and classification of the data sets are based. It provides prioritized customer requirement information for HOQ analysis (Li and Wang, 2010). A classification tree algorithm can map customer requirements to functional requirement templates with accurate and comprehensible rules (Du et al , 2003).…”

Section: Introductionmentioning

confidence: 99%

Dynamic customer preference analysis for product portfolio identification using sequential pattern mining

Zhang

Shen

2017

IMDS

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Purpose In the initial stage of product design, product portfolio identification (PPI) aims to translate customer needs (CNs) into product specifications (PSs). This is an essential task, since understanding what customers really want is at the center of product design. However, design information is incomplete and design knowledge is minimal during this stage. Furthermore, PPI is often a confusing and frustrating task, especially when customer preferences are changing rapidly. To facilitate the task, the purpose of this paper is to capture the time-sensitive mapping relationship between CNs and PSs. Design/methodology/approach This paper proposes a design sequential pattern mining model to uncover implicit but valuable knowledge from chronological transaction records. First, CNs and PSs from these records are transformed and connected according to the transaction time. Second, procedures such as litemset generation, data transformation and pattern mining are conducted based on the AprioriAll algorithm. Third, the uncovered patterns are modified and applied by engineers. Findings Using the retrieved patterns, engineers can keep up with the dynamics of customer preferences with regard to different PSs. Research limitations/implications Computational experiments on a case study of customization of desktop computers show that the proposed method is capable of extracting useful sequential patterns from a design database. Originality/value Considering the times tamps of the transactions, a sequential pattern mining-based method is proposed to extract valuable patterns. These patterns can help engineers identify market trends and the correlation among PSs.

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A rough set based data mining approach for house of quality analysis

Cited by 11 publications

References 19 publications

Data-Driven Design in Concept Development: Systematic Review and Missed Opportunities

Data-Driven Design in Concept Development: Systematic Review and Missed Opportunities

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

Dynamic customer preference analysis for product portfolio identification using sequential pattern mining

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