The purpose of this paper is to develop an unsupervised classification algorithm including feature selection for industrial product classification with the basic philosophy of a supervised Mahalanobis‐Taguchi System (MTS).
Two novel unsupervised classification algorithms called Unsupervised Mahalanobis Distance Classifier (UNMDC) are developed based on Mahalanobis' distance for identifying “abnormals” as individuals (or, groups) including feature selection. The identification of “abnormals” is based on the concept of threshold value in MTS and the distribution property of Mahalanobis‐D2.
The performance of this algorithm, in terms of its efficiency and effectiveness, has been studied thoroughly for three different types of steel product on the basis of its composition and processing parameters. Performance in future diagnosis on the basis of useful features by the new scheme is found quite satisfactory.
This new algorithm is able to identify the set of significant features, which appears to be always a larger class than that of MTS. In industrial environment, this algorithm can be implemented for continuous monitoring of “abnormal” situations along with the general concept of screening “abnormals” either as individuals or as groups during sampling.
The concept of determining threshold for diagnostic purpose is algorithm dependent and independent of the domain knowledge, hence much more flexible in large domain. Multi‐class separation and feature selection in case of detection of abnormals are the special merits of this algorithm.
