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Association Rule Generation Using Apriori Mend Algorithm for Student’s Placement

D. Magdalene Delighta Angeline, I. Samuel Peter James


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.


Apriori Mend Algorithm, Association Rules, Data Mining, Knowledge Discovery.

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