Knowledge discovery from supplier change control data for purchasing management

R. G. Davis, Jennie Si

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The self-organizing map (SOM) is a powerful neural network tool for analyzing multivariable data. It reveals the interrelations within the variables through an iterative learning process. The clustering and topology preserving properties have made the SOM an ideal tool to exploring large datasets (large in both attributes and data records). This paper focuses on using a real life manufacturing dataset about changes to a product or process made by suppliers of the company. We use results from this analysis to show what SOM can provide as in depth understanding of the dataset. We also provide techniques to encode symbolic variables into forms that the SOM can admit. Procedures are also provided to interpret the SOM output results.

Original languageEnglish (US)
Title of host publication2001 International Conferences on Info-Tech and Info-Net: A Key to Better Life, ICII 2001 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages67-72
Number of pages6
Volume3
ISBN (Electronic)0780370104, 9780780370104
DOIs
StatePublished - 2001
EventInternational Conferences on Info-Tech and Info-Net, ICII 2001 - Beijing, China
Duration: Oct 29 2001Nov 1 2001

Other

OtherInternational Conferences on Info-Tech and Info-Net, ICII 2001
CountryChina
CityBeijing
Period10/29/0111/1/01

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Signal Processing
  • Computers in Earth Sciences
  • Control and Systems Engineering
  • Instrumentation

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  • Cite this

    Davis, R. G., & Si, J. (2001). Knowledge discovery from supplier change control data for purchasing management. In 2001 International Conferences on Info-Tech and Info-Net: A Key to Better Life, ICII 2001 - Proceedings (Vol. 3, pp. 67-72). [983037] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICII.2001.983037