## 1 Introduction  
Safety analysis focuses on identifying the most significant factors that affect the occurrence of an incident [1, 2]. Analyzing occupational incidents based on industry and injury characteristics is important for finding causes of accidents, and managing prevention planning [3]. Occupational injury management is significant from organizational, engineering, and economic points of view in an industry [4]. Applying data analytics to identify risk groups in an organization results not only in optimizing workers' productivity, but also in safety improvement through the targeted control of occupational injuries and illnesses [5]. Injury risk analysis allows investigating modifiable occupational injuries by focusing on intervention [6]. The prediction of occupational incidents is an important task for any industry [7]. Since occupational incidents affect workers' lives, both in and out of work, and impose a considerable economic burden on employers, employees, medical care systems, insurance companies, and society, taking planned actions to reduce the frequency of occupational incidents is necessary [8]. With the purpose of reducing the frequency and severity of occupational incidents and improving occupational safety, researchers aim to identify causes and mechanisms of the occurrence of occupational incidents [9; 10]. Introducing any preventive measure to reduce the risk of occupational injuries is based on a correct assessment of risk components using quantitative methods [11]. Data mining techniques contribute to deriving actionable conclusions from empirical data to improve workplace safety [4]. Understanding the factors influencing the occupational incidents is the first step in the process of preventing incidents and improving workplace safety [9]. In addition, analyzing trends and patterns of occupational incidents helps to develop effective and actionable incident prevention strategies and reduce or eliminate workplace injuries [2]. Incident severity prediction models are crucial for improving safety [12]. In incident and injury severity analysis, identifying subgroups of incidents with homogeneous categorical variables is significant in determining factors that contribute to incident severity [13]. Furthermore, one of the principle objectives in occupational safety analysis is to identify the key factors that affect the severity of an incident. Most datasets with information about incidents have the issue of heterogeneity in the data [14]. Thus, clustering is a beneficial method in analyzing incident factors, and gaining information about those variables that are statistically significant [15]. Latent class clustering for identifying injury/incident patterns has been used mostly in the field of traffic accidents or crash severity analysis. An analysis of cyclist-motorist crashes between 2007-2011 in Denmark revealed 13 distinguished latent classes, and contributed to investigating their prevalence and severity [14]. This analysis showed the features that distinguished the latent classes included incident factors such as speed limit, helmet wearing behavior, and road surface conditions. After determining latent classes of cyclists-motorists, the severity of injuries per latent class was analyzed. A similar study performed in Italy [13] on cyclist crashes between 2011-2013 applied latent class clustering to identify distinguished subgroups of crashes with categorical variables including road infrastructure, road user, vehicle, and environmental and time period variables. They segmented the cyclist crashes into 19 subgroups, each representing a different crash type. Another study used crash data on highway-railway crossings between 1997-2006 in the USA [16], and segmented the injury risk groups of such crashes using the latent class clustering approach. The results identified the most influencing factors on crash occurrence, separating the injury risk groups include the driver's age, as well as the presence of rain or snow, time of the crash, and motorist's actions prior to the incident [16].