ECLAT based association rule mining for advancing workplace mental health and organizational insights.

This study explores the application of the equivalence class transformation (ECLAT) algorithm for frequent itemset mining and association rule discovery in the analysis of employee mental health data within the workplace. As mental health continues to be recognized as a crucial factor influencing workplace productivity, understanding the factors that impact employee well-being is of paramount importance. To address this, we apply the ECLAT algorithm, a technique known for its efficiency in handling large datasets through vertical transaction representation and depth-first search methods. Unlike traditional algorithms, ECLAT minimizes computational complexity and overhead, allowing for the rapid identification of frequent itemsets. By analyzing the dataset, the ECLAT algorithm identifies the most frequent combinations of these factors, and association rules are derived to uncover the relationships between various mental health elements. These rules are assessed using support, confidence, and lift metrics to determine their significance. The results offer critical insights into the influence of workplace policies on employee mental health, providing a foundation for organizations to create more supportive environments that promote mental well-being. Ultimately, this research contributes to advancing workplace well-being initiatives, fostering a mentally healthy workforce, and driving organizational success.