Decision support system for safety improvement: an approach using multiple correspondence analysis, t-SNE algorithm and K-means clustering

Abstract

An attempt has been made to develop a decision support system (DSS) for safety improvement using a multi-step knowledge discovery process involving multiple correspondence analysis (MCA), t-SNE algorithm and K-means clustering. MCA is used for dimension reduction and perceptual mapping from categorical data. Usually, the first two dimensions are used for perceptual mapping if these two dimensions explain a significant percentage of variance. Otherwise, the traditional method of two dimensional mapping, leads to loss of important categorical information involved with other dimensions. Considering the above, a novel R2-profile approach, as an alternate to inertia based approach, is adopted to obtain the desired number of dimensions to be retained without loss of significant amount of information. t-SNE technique reduces the high dimensional data into two dimensional (2D) map, which provides the associations amongst different categories. K-means clustering grouped the 2D categories in homogenous clusters as per the similarities of the categories. A novel kernel category based chi-square distance method is proposed to identify sub-clusters within a cluster which subsequently provides useful rules for safety improvement. The methodology also provides a logical approach of dimension reduction in a form called ‘funnel diagram’. Finally, the DSS is applied to analysing near miss incidents occurred in electric overhead traveling (EOT) crane operations in a steel plant. Several safety rules are identified and safety interventions are proposed.