document: Mining statistically important equivalence classes and delta-discriminative emerging patterns The support-confidence framework is the most common measure used in itemset mining algorithms , for its antimonotonicity that effectively simplifies the search lattice . This computational convenience brings both quality and statistical flaws to the results as observed by many previous studies . In this paper , we introduce a novel algorithm that produces itemsets with ranked statistical merits under sophisticated test statistics such as chi-square , risk ratio , odds ratio , etc. . Our algorithm is based on the concept of equivalence classes . An equivalence class is a set of frequent itemsets that always occur together in the same set of transactions . Therefore , itemsets within an equivalence class all share the same level of statistical significance regardless of the variety of test statistics . As an equivalence class can be uniquely determined and concisely represented by a closed pattern and a set of generators , we just mine closed patterns and generators , taking a simultaneous depth-first search scheme . This parallel approach has not been exploited by any prior work . We evaluate our algorithm on two aspects . In general , we compare to LCM and FPclose which are the best algorithms tailored for mining only closed patterns . In particular , we compare to epMiner which is the most recent algorithm for mining a type of relative risk patterns , known as minimal emerging patterns . Experimental results show that our algorithm is faster than all of them , sometimes even multiple orders of magnitude faster . These statistically ranked patterns and the efficiency have a high potential for real-life applications , especially in biomedical and financial fields where classical test statistics are of dominant interest .
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