Abstract
In this paper I 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 quite easy to implement, but also very convenient to execute on external storage, thus rendering it a highly useful method if the transaction database to mine cannot be loaded into main memory. Furthermore, I review RElim (an algorithm I proposed in an earlier paper and improved in the meantime) and discuss different optimization options for both SaM and RElim. Finally, I present experiments comparing SaM and RElim with classical frequent item set mining algorithms (like Apriori, Eclat and FP-growth).
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Borgelt, C. (2010). Simple Algorithms for Frequent Item Set Mining. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds) Advances in Machine Learning II. Studies in Computational Intelligence, vol 263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05179-1_16
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