A data mining approach to improve phase I process control

Chang W. Kang, Thuntee Sukchotrat, Seoung Bum Kim

Research output: Contribution to conferencePaper

Abstract

We propose a data mining approach to improve the phase I analysis of statistical process control. We use the clustering analysis to make initial groups of the historical data, followed by the classification analysis, along with the synthetic datasets to further purify the in-control data. The simulation study shows that our proposed approach performs better than a traditional control chart technique.

Original languageEnglish
Pages192-196
Number of pages5
Publication statusPublished - 2007
EventIIE Annual Conference and Expo 2007 - Industrial Engineering's Critical Role in a Flat World - Nashville, TN, United States
Duration: 2007 May 192007 May 23

Other

OtherIIE Annual Conference and Expo 2007 - Industrial Engineering's Critical Role in a Flat World
CountryUnited States
CityNashville, TN
Period07/5/1907/5/23

Keywords

  • Classification analysis
  • Clustering analysis
  • Data mining
  • Multivariate control chart
  • Phase I analysis

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Fingerprint Dive into the research topics of 'A data mining approach to improve phase I process control'. Together they form a unique fingerprint.

  • Cite this

    Kang, C. W., Sukchotrat, T., & Kim, S. B. (2007). A data mining approach to improve phase I process control. 192-196. Paper presented at IIE Annual Conference and Expo 2007 - Industrial Engineering's Critical Role in a Flat World, Nashville, TN, United States.