RElim - Frequent Item Set Mining

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Description

RElim is a program to find frequent item sets (also closed and maximal) with the relim algorithm (recursive elimination), which is inspired by the FP-growth algorithm, but does its work without prefix trees or any other complicated data structures. The main strength of this algorithm is not its speed (although it is not slow, but even outperforms Apriori and Eclat on some data sets), but the simplicity of its structure. Basically all the work is done in one recursive function of fairly few lines of code.

Note that the current version of this program can only find frequent item sets, not association rules.

Full description of the RElim program (included in the source package).

If you have trouble executing the program on Microsoft Windows, check whether you have the Microsoft Visual C++ Redistributable for Visual Studio 2022 (see under "Other Tools and Frameworks") installed, as the program was compiled with Microsoft Visual Studio 2022.

Papers that describe the algorithm/implementation:

• Keeping Things Simple: Finding Frequent Item Sets by Recursive Elimination
  Christian Borgelt.
  Workshop Open Source Data Mining Software (OSDM'05, Chicago, IL), 66-70.
  ACM Press, New York, NY, USA 2005
  relim.pdf (166 kb) relim.ps.gz (122 kb) (5 pages)
• Simple Algorithms for Frequent Item Set Mining
  Christian Borgelt
  Chapter 16 of: J. Koronacki, Z.W. Raz, S.T. Wierzchon, and J.K. Kacprzyk (eds.)
  Advances in Machine Learning II (Studies in Computational Intelligence 263), 351-369
  Springer-Verlag, Berlin, Germany 2010
  doi:10.1007/978-3-642-05179-1_16 www.springerlink.com
  simple.pdf (416 kb) simple.ps.gz (261 kb) (18 pages)

An extension of the algorithm to mine fuzzy frequent item sets (which is available as part of the above program, but was originally implemented as a separate program relx) has been developed in:

• Mining Fuzzy Frequent Item Sets
  Xiaomeng Wang, Christian Borgelt, and Rudolf Kruse
  Proc. 11th Int. Fuzzy Systems Association World Congress (IFSA'05, Beijing, China), 528-533.
  Tsinghua University Press and Springer-Verlag, Beijing, China, and Heidelberg, Germany 2005.
  ifsa_05.pdf (107 kb) ifsa_05.ps.gz (70 kb) (6 pages)
• Fuzzy Frequent Pattern Discovery Based on Recursive Elimination
  Xiaomeng Wang, Christian Borgelt, and Rudolf Kruse
  Proc. 4th IEEE Int. Conf. on Machine Learning and Applications (ICMLA'05, Los Angeles, CA), 391-396.
  IEEE Press, Piscataway, NJ, USA 2005
  icmla_05.pdf (159 kb) icmla_05.ps.gz (140 kb) (6 pages)

More information about frequent item set mining, implementations of other algorithms as well as test data sets can be found at the Frequent Itemset Mining Implementations Repository.