Author(s)

E. Tuv & G. Runger

Abstract

Learning patterns through artificial contrasts with application to process control E. Tuvl & G. ~unger' ~nalysis & Control Technology, Intel Corporation, USA "epartment of industrial Engineering, Arizona State University, USA Abstract In manufacturing as well as other application areas there is a need to learn standard operating conditions in order to detect future changes or deviations. This is related to the even more general problem of detecting instances (cases, records) that are unusual compared to the bulk of the data (outliers). Examples of the problem are fault detection in chemical engineering and statistical process control. The outlier problem is ubiquitous. If specific deviations are not a priori specified, this is a type of unsupervised learning problem. The focus here is on the important, practical case for modem data environments. That is, training data with multiple (usual many) variables of mixed types (without the expedient assumptions common in statistics of multivariate nor

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