Measuring Positive and Negative Association of Apriori Algorithm with Cosine Correlation Analysis

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Dewi Wisnu Wardani

Abstract

This work aims to see the positive association rules and negative association rules in the Apriori algorithm by using cosine correlation analysis. The default and the modified Association Rule Mining algorithm are implemented against the mushroom database to find out the difference of the results. The experimental results showed that the modified Association Rule Mining algorithm could generate negative association rules. The addition of cosine correlation analysis returns a smaller amount of association rules than the amounts of the default Association Rule Mining algorithm. From the top ten association rules, it can be seen that there are different rules between the default and the modified Apriori algorithm. The difference of the obtained rules from positive association rules and negative association rules strengthens to each other with a pretty good confidence score.

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Wardani DW. Measuring Positive and Negative Association of Apriori Algorithm with Cosine Correlation Analysis. Baghdad Sci.J [Internet]. [cited 2021Mar.4];18(3):0554. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/4906
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