An innovative integrated modelling of safety data using multiple correspondence analysis and fuzzy discretization techniques

Abstract In this study, we have proposed an innovative integrated methodology to handle a mix of categorical and numeric safety data. We have augmented the traditional multiple correspondence analysis (MCA) through the use of fuzzy discretization approach, t-SNE technique and fuzzy c-means clustering. The fuzzy discretization approach transforms the continuous variables to categorical variables to make them analyzable using MCA. R2-profile is adopted to obtain the best number of hidden dimensions representing the maximum categorical information. Then, t-SNE technique is used to represent the high dimensional categorical information in a 2D map to visualize the significant categorical associations. Then, fuzzy c-means clustering (FCM) is used to group the categories in different clusters based on their membership degree. To determine the optimal number of clusters, cluster validity indices are used. Davies-Bouldin (DB) Index, Dunn’s (DU) Index and Silhouette (SW) coefficients are used to determine the quality of clustering solutions. The proposed methodology is tested using electric overhead traveling (EOT) crane related near-miss incidents and found that our approach is effective. From managerial implication point of view, several safety rules are generated and subsequent safety countermeasures are proposed. Further, the results obtained through FCM is compared with K-means (KM) algorithm and unsupervised fuzzy c-means clustering (UPFCM). FCM outperforms KM and UPFCM on the basis of quality of solutions.