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Data Mining and Fuzzy Clustering to Support Product Family Design

[+] Author Affiliations
Seung Ki Moon, Soundar R. T. Kumara, Timothy W. Simpson

Pennsylvania State University, University Park, PA

Paper No. DETC2006-99287, pp. 317-325; 9 pages
  • ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1: 32nd Design Automation Conference, Parts A and B
  • Philadelphia, Pennsylvania, USA, September 10–13, 2006
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 0-7918-4255-X | eISBN: 0-7918-3784-X
  • Copyright © 2006 by ASME


In mass customization, data mining can be used to extract valid, previously unknown, and easily interpretable information from large product databases in order to improve and optimize engineering design and manufacturing process decisions. A product family is a group of related products based on a product platform, facilitating mass customization by providing a variety of products for different market segments cost-effectively. In this paper, we propose a method for identifying a platform along with variant and unique modules in a product family using data mining techniques. Association rule mining is applied to develop rules related to design knowledge based on product function, which can be clustered by their similarity based on functional features. Fuzzy c-means clustering is used to determine initial clusters that represent modules. The clustering result identifies the platform and its modules by a platform level membership function and classification. We apply the proposed method to determine a new platform using a case study involving a power tool family.

Copyright © 2006 by ASME



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