(Approximate) Frequent Item Set Mining Made Simple with a Split and Merge Algorithm

Abstract

In this paper we introduce SaM, a split and merge algorithm for frequent item set mining. Its core advantages are its extremely simple data structure and processing scheme, which not only make it very easy to implement, but also fairly easy to execute on external storage, thus rendering it a highly useful method if the data to mine cannot be loaded into main memory. Furthermore, we present extensions of this algorithm, which allow for approximate or “fuzzy” frequent item set mining in the sense that missing items can be inserted into transactions with a user-specified penalty. Finally, we present experiments comparing our new method with classical frequent item set mining algorithms (like Apriori, Eclat and FP-growth) and with the approximate frequent item set mining version of RElim (an algorithm we proposed in an earlier paper and improved in the meantime).