SaM: A Split and Merge Algorithm for Fuzzy Frequent Item Set Mining

This paper presents SaM, a split and merge algorithm for frequent item set mining. Its distinguishing qualities are an exceptionally simple algorithm and data structure, which not only render it easy to implement, but also convenient to execute on external storage. Furthermore, it can easily be extended to allow for "fuzzy" frequent item set mining in the sense that missing items can be inserted into transactions with a user-specified penalty. In order to demonstrate its performance, we report experiments comparing it with the "fuzzy" frequent item set mining version of RElim (an algorithm we suggested in an earlier paper [15] and improved in the meantime). Keywords-- data mining, frequent item set mining, fuzzy frequent item set, fault tolerant data mining

• Type: Journal
• Year: 2009
• Where: EUSFLAT
• Authors: Christian Borgelt, Xiaomeng Wang
• Keywords: Algorithm, EUSFLAT 2009, Frequent Item, Fuzzy Logic, Mining