View in EDS HTML Full Text PDF Full Text

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

Saved in:
Bibliographic Details
Title: A rough set based data mining approach for house of quality analysis.
Authors: Li, Jing Rong1 (AUTHOR) catherine.jrli@gmail.com, Wang, Qing Hui1 (AUTHOR)
Source: International Journal of Production Research. Apr2010, Vol. 48 Issue 7, p2095-2107. 13p. 11 Charts.
Subjects: Data mining, Database searching, Automatic extracting (Information science), Text mining, Online data processing, Decision support systems
Abstract: As the first phase of quality function deployment (QFD) and the only interface between the customers and product development team, house of quality (HOQ) plays the most important role in developing quality products that are able to satisfy customer needs. No matter in what shape or form HOQ can be built, the key to this process is to find out the hidden relationship between customers' requirements and product design specifications. This paper presents a general rough set based data mining approach for HOQ analysis. It utilises the historical information of customer needs and the design specifications of the product that was purchased, employs the basic rough set notions to reveal the interrelationships between customer needs and design specifications automatically. Due to the data reduction nature of the approach, a minimal set of customer needs that are crucial for the decision on the correlated design specifications is derived. The end result of the approach is in the form of a minimal rule set, which not only fulfils the goal of HOQ, but can be used as supporting data for marketing purposes. A case study on the product of electrically powered bicycles is included to illustrate the approach and its efficiency. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 49143926
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A rough set based data mining approach for house of quality analysis.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Li%2C+Jing+Rong%22">Li, Jing Rong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> catherine.jrli@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Qing+Hui%22">Wang, Qing Hui</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Apr2010, Vol. 48 Issue 7, p2095-2107. 13p. 11 Charts.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Database+searching%22">Database searching</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+extracting+%28Information+science%29%22">Automatic extracting (Information science)</searchLink><br /><searchLink fieldCode="DE" term="%22Text+mining%22">Text mining</searchLink><br /><searchLink fieldCode="DE" term="%22Online+data+processing%22">Online data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+support+systems%22">Decision support systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: As the first phase of quality function deployment (QFD) and the only interface between the customers and product development team, house of quality (HOQ) plays the most important role in developing quality products that are able to satisfy customer needs. No matter in what shape or form HOQ can be built, the key to this process is to find out the hidden relationship between customers' requirements and product design specifications. This paper presents a general rough set based data mining approach for HOQ analysis. It utilises the historical information of customer needs and the design specifications of the product that was purchased, employs the basic rough set notions to reveal the interrelationships between customer needs and design specifications automatically. Due to the data reduction nature of the approach, a minimal set of customer needs that are crucial for the decision on the correlated design specifications is derived. The end result of the approach is in the form of a minimal rule set, which not only fulfils the goal of HOQ, but can be used as supporting data for marketing purposes. A case study on the product of electrically powered bicycles is included to illustrate the approach and its efficiency. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=49143926
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/00207540802665907
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 2095
    Subjects:
      – SubjectFull: Data mining
        Type: general
      – SubjectFull: Database searching
        Type: general
      – SubjectFull: Automatic extracting (Information science)
        Type: general
      – SubjectFull: Text mining
        Type: general
      – SubjectFull: Online data processing
        Type: general
      – SubjectFull: Decision support systems
        Type: general
    Titles:
      – TitleFull: A rough set based data mining approach for house of quality analysis.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Li, Jing Rong
      – PersonEntity:
          Name:
            NameFull: Wang, Qing Hui
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 04
              Text: Apr2010
              Type: published
              Y: 2010
          Identifiers:
            – Type: issn-print
              Value: 00207543
          Numbering:
            – Type: volume
              Value: 48
            – Type: issue
              Value: 7
          Titles:
            – TitleFull: International Journal of Production Research
              Type: main
ResultId 1