@inproceedings{25cf05af43cf4da09ff51c37768d1ccf,
title = "Knowledge discovery from supplier change control data for purchasing management",
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.",
author = "Davis, {R. G.} and Jennie Si",
note = "Funding Information: Supported in part by NSF under grant ECS-9553202 and ECS-0002098. Funding Information: Supported in part by NSF under grant ECS-9553202 and ECS-0002098. The first author now is with Intel Corp. Publisher Copyright: {\textcopyright} 2001 IEEE.; International Conferences on Info-Tech and Info-Net, ICII 2001 ; Conference date: 29-10-2001 Through 01-11-2001",
year = "2001",
doi = "10.1109/ICII.2001.983037",
language = "English (US)",
series = "2001 International Conferences on Info-Tech and Info-Net: A Key to Better Life, ICII 2001 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "67--72",
editor = "Zhongzhi Shi and Hui Li and Y.X. Zhong",
booktitle = "2001 International Conferences on Info-Tech and Info-Net",
}