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Developing an unsupervised classification algorithm for characterization of steel properties

Prasun Das (SQC and OR Division, Indian Statistical Institute, Kolkata, India)
Shubhabrata Datta (Production Engineering Department, Birla Institute of Technology Extension Centre, Deoghar, India)

International Journal of Quality & Reliability Management

ISSN: 0265-671X

Article publication date: 13 April 2012

326

Abstract

Purpose

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).

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Originality/value

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.

Keywords

Citation

Das, P. and Datta, S. (2012), "Developing an unsupervised classification algorithm for characterization of steel properties", International Journal of Quality & Reliability Management, Vol. 29 No. 4, pp. 368-383. https://doi.org/10.1108/02656711211224839

Publisher

:

Emerald Group Publishing Limited

Copyright © 2012, Emerald Group Publishing Limited

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