A data mining approach to improve phase I process control

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.