Analysing the quality of Association Rules by Computing an Interestingness Measures

Objective: Association rule mining is one of the data mining process for discovering frequent item set between transaction databases. The main objective of this research work is statistically analyses the quality rules in the apriori algorithm of association rule mining. Methods: An Interestingness measures is a subset of statistical method and it can give the solution for splitting interesting rules within huge association rules. Currently, it has shown around hundred and above measures. Specifically, this study is to concentrate on eight measures such as lift, chi-square, hyper-lift, hyper-confidence, conviction, coverage, leverage and cosine. In this analysis is performed in two places of real databases whereas Agriculture and Medical domain. Findings: At the experimental results, the proposed system is rectified that the problem many interesting rules are eliminated in satisfying the threshold value of support and confidence. Therefore, the user do not confirm that the strength of interest rules may be least by setting the low threshold value. The comparison and correlation measures also obtained along with the interesting rules. There are some measures outperformed than other and thus measures can mostly correlate with the order lift, chi-squared, hyper-lift, hyper-confidence and conviction. The performance of this work is consistently checking in difference size of transaction databases in addition to we identify the unresolved problem of apriori algorithm. Conclusion: Finally, this research concludes that statistical interestingness measures are really helpful for finding interesting rules among large association rules.