Journal of Theoretical and Applied Information Technology 28 th February 2018. Vol.96. No 4 © 2005 – ongoing JATIT & LLS ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 875 CLUSTERING FAILED COURSES OF ENGINEERING STUDENTS USING ASSOCIATION RULE MINING ROSEMARIE M. BAUTISTA Associate Professor, Bulacan State University, College of Information and Communications Technology, Philippines Email: rosemarie.bautista@bulsu.edu.ph ABSTRACT In today’s world, the fast-paced changes in technology and upswing volume of organizational data in almost all domains including academe are very remarkable. This coupled with the aspiration to gain competitive advantage necessitate the utilization of data mining. This paper applies the processes in the Knowledge Discovery in Databases by Fayyad and presents in methodological way the steps performed towards finding the associations between courses failed by engineering students. It started with the preparation of data moving towards proper transformation of it for data mining and concluding with data interpretation and evaluation. Using association rule mining through Apriori algorithm, the rules were extracted from the database. The statistical significance and the strength of the rule were analyzed using 3 measures of usefulness: lift, support and confidence. All the rules generated have positive co-relation, that is, the relationships of the consequent of the rule with the antecedent are not due to chance. The over-all output of the study is expected to offer viable results that may be used by administrator, academic advisor and curriculum planners in devising worth-while strategies such as improvement of teaching methodology, re-structure of curriculum, modification of course pre-requisites or development of supplemental activities to students. Keywords: Data Mining, Association Rule Mining, Market Basket Analysis, Knowledge Discovery in Databases, Educational Data Mining 1. INTRODUCTION The hasty growth of technology and the surge of enormous data are increasingly evident in the past few decades. The widespread computerization and explosive growth of data in almost every conceivable field including government, businesses, recreations, education and others necessitate the need to analyze, manage and transform such data into usable information. Data mining, thus, becomes an imperative approach in these integral tasks [1]. Data mining or DM which is usually referred to as the process of discovering invaluable knowledge from databases [2], [3] is part of the natural evolution of information technology [1]. It oftentimes uncovers hidden patterns which are not usually generated by traditional computer-based information system. It is termed as educational data mining or EDM in the context of education. EDM is now gaining popularity and capturing the interest of countless researchers who wish to extract previously unknown patterns and unique information from educational databases [4], [5]. It is used to analyze students’ historical attributes to gain understanding of some academic records, enrolment data, student behavior and pedagogical performance that may improve decision-making and better management of scholastic-related issues [6], [7], [8]. Techniques such as association rule, k means and some classification algorithms were used to analyze both students’ behavior and teachers’ performance to provide recommendation for further curriculum improvement [9]. Among the thrust of almost every university is continuous quest for excellence which may be done by constant revisit of curriculum. The Bulacan State University (BulSU) shares the same unwavering commitment. One of its prides is the College of Engineering (COE) however also considered one of those with the highest students’