document: Screening and interpreting multi-item associations based on log-linear modeling Association rules have received a lot of attention in the data mining community since their introduction . The classical approach to find rules whose items enjoy high support ( appear in a lot of the transactions in the data set ) is , however , filled with shortcomings . It has been shown that support can be misleading as an indicator of how interesting the rule is . Alternative measures , such as lift , have been proposed . More recently , a paper by DuMouchel et al. proposed the use of all-two-factor loglinear models to discover sets of items that can not be explained by pairwise associations between the items involved . This approach , however , has its limitations , since it stops short of considering higher order interactions ( other than pairwise ) among the items . In this paper , we propose a method that examines the parameters of the fitted loglinear models to find all the significant association patterns among the items . Since fitting loglinear models for large data sets can be computationally prohibitive , we apply graph-theoretical results to divide the original set of items into components ( sets of items ) that are statistically independent from each other . We then apply loglinear modeling to each of the components and find the interesting associations among items in them . The technique is experimentally evaluated with a real data set ( insurance data ) and a series of synthetic data sets . The results show that the technique is effective in finding interesting associations among the items involved .
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