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

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

Christian Borgelt (European Center for Soft Computing, Spain) and Xiaomeng Wang (Otto-von-Guericke University of Magdeburg, Germany)
DOI: 10.4018/978-1-60566-858-1.ch010
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Abstract

In this chapter the authors 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, the authors 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, they present experiments comparing their 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 the authors proposed in an earlier paper and improved in the meantime).
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Frequent Item Set Mining

Frequent item set mining is a data analysis method that was originally developed for market basket analysis. It aims at finding regularities in the shopping behavior of the customers of supermarkets, mail-order companies and online shops. In particular, it tries to identify sets of products that are frequently bought together. Once identified, such sets of associated products may be exploited to optimize the organization of the products on the shelves of a supermarket or on the pages of a mail-order catalog or web shop, may be used to suggest other products a customer could be interested in, or may give hints which products may conveniently be bundled.

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