Regression and Correlation Analysis of Different Interestingness Measures for Mining Association Rules (International Journal of Innovative Research in Computer Science & Technology, 2018)
Association Rule Mining is the significant  way to extract knowledge from data sets. The  association among the instance of a dataset can  measured with Interestingness Measures (IM) metrics.  IM define how much interesting the extract knowledge  is. Researchers have proved that the classical  Support-Confidence metrics can’t extract the real  knowledge and they have been proposing different IM.  From a user perspective it’s really tough to select the  minimal and best measures from them. From our  experiment, the correlation among the various IM such  as Support, Confidence, Lift, Cosine, Jaccard,  Leverage etc. are evaluated in different popular data  sets. In this paper our contribution is to find the  correlation among the IM with different ranges in  different types of data sets which were not applied in  past researches. This study also identified that the  correlation varies from data set to data set and  proposed a solution based on multiple criterion that  will help the users to select the minimal and best from  a large number of IM.
Keywords: Association rules; correlation; interestingness measures; regression analysis