This research focuses on predicting academician performance in terms of publication productivity and investigate the factors that affect academiciansâ€™ achievement. Previous studies have shown that there are many important variables when analysing academiciansâ€™ productivity at the individual level. This study investigates how scientific publication rate by individual is influenced by factors such as gender, age, number of research grant and academic position of the researchers using decision tree. Having a decision rules, university leaders can understand upcoming trends with respect to leadership requirements and academicians needs. It is also helping university managements understand challenges and therefore can deploy the right strategies for human resource management interventions. The discovered knowledge among the attributes obtained from mining the data can be used to predict the university productivity output. The study, involving almost 3000 university lecturers, shows a number of interesting patterns that can be used for predicting publicatio output.
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