Application of association rule mining in investigating the interaction between evaluation indicators in the performance measurement system: a case study

Different organizations, companies, and businesses evaluate the performance of their personnel using several indicators during evaluation intervals. Generally, indicators are categorized by evaluation forms, and evaluation results are extracted based on them. Therefore, this method of classification may not reveal the logical relationship between indicators. As a result, it is essential to investigate the behavior of an organization’s performance indicators more accurately and make the dependence between indicators clearer to managers. According to the generated information throughout the evaluation process, the interaction between evaluation indicators can be analyzed, using association rule mining. In this paper, we propose the Apriori algorithm to discover frequent relationships between evaluation indicators over a time span. Further, the performance of this approach is evaluated by a real large dataset from TESMA organizational performance management software.

Keywords:

Association rule mining , Performance measurement system , Apriori algorithm