Interesting Knowledge Exploring for Product Component Selection

Author

Yu, Li ; Chen, Yun ; Liu, Yuebo

Author_Institution

Acad. of Modern Service Ind., Shanghai Univ. of Finance & Econ., Shanghai, China

fYear

2009

fDate

19-20 Dec. 2009

Firstpage

1

Lastpage

4

Abstract

During the product configuration design stage, it is important to find useful knowledge to reduce the dedicated efforts of engineers. To fulfill this task, the quality and interestingness of knowledge plays the decisive part. This paper utilizes Apriori algorithm to mine rules from historical data base. In addition the traditional criterions support and confidence, the criterion interestingness is also applied to filter rules that might misguide design engineers. The validity of the proposed model is illustrated by a case of electrical bicycles.

Keywords

algorithm theory; data mining; operations research; product design; product development; Apriori algorithm; historical database; interesting knowledge exploration; interestingness criterion; product component selection; product configuration design stage; rule mining; support-confidence criterion; Algorithm design and analysis; Association rules; Bicycles; Bills of materials; Data mining; Design engineering; Filters; Finance; Knowledge engineering; Product design;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4994-1

Type

conf

DOI

10.1109/ICIECS.2009.5364171

Filename

5364171