Association Rule Generation Using Apriori Mend Algorithm for Student's Placement
D. Magdalene Delighta Angeline, I. Samuel Peter James · CiiT international journal of data mining and knowledge engineering · 2011
Association rules reflect the inner relationship of data. Discovering these associations is beneficial to the correct and appropriate decision made by decision-makers. It also provides an effective means to found the potential link between the data, reflecting a built-in association between the data. In order to show the effective relation of data, student placement was chosen and experiments were carried out which shows the best rules with 92.86% confidence while comparing with the previous Apriori approach. In this paper Apriori Mend algorithm was discussed which provide better result in mining association rules for Student’s placement in industry.