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.
Highlights
Association rule analysis is used to obtain association rules which often appear in dataset [1]
The experiments are conducted by extracting positive association rules (PAR) and negative association rules (NAR) in the support-confidence framework with Cosine correlation analysis using Apriori algorithm for frequent itemsets mining
This research will perform PAR mining using the default Association Rule Mining (ARM) algorithm with Apriori algorithm for frequent itemsets mining on a support-confidence framework
Summary
Association rule analysis is used to obtain association rules which often appear in dataset [1]. Its argued that with the proper analysis, NAR will strengthen the positive association rules. To measure the certainty and usability level of a rule, support and confidence score are used. Those measurements are not good enough because confidence is only a conditional probability prediction value of two or more item sets [2]. They cannot measure the correlation value between two item sets. It may cause misleading association analysis results and it can cause more significant problems later on the extended applications
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