|32 bit||64 bit||(32/64 bit only for executable)|
|eclat||(481 kb)||eclat||(503 kb)||GNU/Linux executable|
|eclat.exe||(297 kb)||eclat.exe||(344 kb)||Windows console executable|
|eclat.zip||(253 kb)||eclat.tar.gz||(230 kb)||C sources, version 5.17 (2016.10.15)|
|pyfim.py||(13 kb)||Python source of a simplified version|
|census.zip||(382 kb)||census data set (UCI ML repository)|
|census||(2 kb)||shell script used for the conversion|
Eclat is a program to find frequent item sets (also closed and maximal as well as generators) with the Eclat algorithm [Zaki et al. 1997], which carries out a depth first search on the subset lattice and determines the support of item sets by intersecting transaction lists. This implementation also supports diffsets [Zaki and Gouda 2003] and several other algorithm variants, including certain variants of LCM (Linear-time Closed itemset Miner) [Uno et al. 2003], [Uno et al. 2004], [Uno et al. 2005], which employs an occurrence deliver scheme to determine the support of item sets. Since version 5.0 the program made available above can also be used to find association rules.
The Python version is simplified, much slower, and supports much fewer program options. It may be useful as an illustrative implementation that demonstrates some core features of the Eclat algorithm. However, if you want to use frequent item set mining in Python for actual mining tasks, it is recommended to use on the PyFIM library, which provides a Python interface to the C implementation.
Full description of the Eclat 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 Packages for Visual Studio 2015 installed, as the library was compiled with Microsoft Visual Studio 2015.
Papers that describe this algorithm and some implementation aspects of the C version (note that due to a complete redesign in version 3.0, the description in [Borgelt 2003], does not fit the program anymore):
Some other references:
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.