Abstract
In this paper, the proposed Unsupervised Mahalanobis Distance Classifier (UNMDC) scheme is a multi-class unsupervised classifier with the basic philosophy of supervised Mahalanobis?Taguchi System (MTS) based monitoring procedure. A comparative study between the MTS and the proposed UNMDC is performed with various simulated experiments for different types of correlation structure and location parameters, published data and real-life data sets of different sizes and dimensions. The advantages of domain knowledge independent thresholds, multi-class separation, identifying process shifts during multivariate process-monitoring and feature selection in case of detection of abnormals are the special merits of this algorithm.
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