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

被引：0

作者：

Borgelt, Christian ^{[1
]}

Wang, Xiaomeng ^{[2
]}

机构：

[1] European Ctr Soft Comp, C Gonzalo Gutierrez Quiros S-N, Mieres 33600, Spain

[2] Otto von Guericke Univ, D-39106 Magdeburg, Germany

来源：

PROCEEDINGS OF THE JOINT 2009 INTERNATIONAL FUZZY SYSTEMS ASSOCIATION WORLD CONGRESS AND 2009 EUROPEAN SOCIETY OF FUZZY LOGIC AND TECHNOLOGY CONFERENCE | 2009年

关键词：

data mining; frequent item set mining; fuzzy frequent item set; fault tolerant data mining;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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).

引用

页码：968 / 973

页数：6

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