Students Placement Competency Analysis-An Apriori Framework

Data Mining is an emerging research area, where the goal is to extract significant patterns or interesting rules from large databases. Association rule mining is a famous technique in data mining. The term association rule was first introduced by Agrawal et al. Association rules of are also referred in the literature as classical or boolean association rules. Association rules mining or induction is commonly used in market basket analysis to find items frequently bought together by shoppers. Pattern recognition aims to classify data based either on apriori knowledge or on statistical information extracted from the patterns. This paper aims to find the correlation among attributes for mining rules from student's database. The dataset contains details about placement attributes, students skillset, academic qualification, language competency and personal information. In this work we have implemented the association rule mining algorithm a data mining task the apriori algorithm to find interesting rules. The study is implemented in weka tool to analyze students competency factors related to placement.