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A Text Mining Technique for Manufacturing Supplier Classification

[+] Author Affiliations
Peyman Yazdizadeh, Farhad Ameri

Texas State University, San Marcos, TX

Paper No. DETC2015-46694, pp. V01BT02A036; 7 pages
  • ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1B: 35th Computers and Information in Engineering Conference
  • Boston, Massachusetts, USA, August 2–5, 2015
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5705-2
  • Copyright © 2015 by ASME


The web presence of manufacturing suppliers is constantly increasing and so does the volume of textual data available online that pertains to the capabilities of manufacturing suppliers. To process this large volume of data and infer new knowledge about the capabilities of manufacturing suppliers, different text mining techniques such as association rule generation, classification, and clustering can be applied. This paper focuses on classification of manufacturing suppliers based on the textual description of their capabilities available in their online profiles. A probabilistic technique that adopts Naïve Bayes method is adopted and implemented using R programming language. Casting and CNC machining are used as the examples classes of suppliers in this work. The performance of the proposed classifier is evaluated experimentally based on the standard metrics such as precision, recall, and F-measure. It was observed that in order to improve the precision of the classification process, a larger training dataset with more relevant terms must be used.

Copyright © 2015 by ASME



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