Against-Expectation Pattern Discovery: Identifying Interactions within Items with Large Relative-Contrasts in Databases

 Abstract—We design a new algorithm for identifying against-expectation patterns. An against-expectation pattern is either an itemset whose support is out of a range of the expected support value, referred to as an against-expectation itemset, or it is an association rule generated by an against-expectation itemset, referred to as an against-expectation rule. Therefore, against-expectation patterns are interactions within those items whose supports have large relative-contrasts in a given database. We evaluate our algorithms experimentally, and demonstrate that our approach is efficient and promising