|32 bit||64 bit||(32/64 bit only for executable)|
|jim||(277 kb)||jim||(284 kb)||GNU/Linux executable|
|jim.exe||(179 kb)||jim.exe||(211 kb)||Windows console executable|
|jim.zip||(169 kb)||jim.tar.gz||(151 kb)||C sources, version 3.17 (2018.03.21)|
|census.zip||(382 kb)||census data set (UCI ML repository)|
|census||(2 kb)||shell script used for the conversion|
JIM is a program to find Jaccard item sets with an extension of the Eclat algorithm. In analogy to frequent item set mining, where one tries to find item sets the support of which exceeds a user-specified threshold (minimum support) in a database of transactions, a Jaccard item set is an item set for which the (generalized) Jaccard index of its item covers exceeds a user-specified threshold. This measure yields a much better assessment of the association strength of the items than simple support. Since the (generalized) Jaccard index is, like the support, also anti-monotone, the same basic approach can be used for the search, provided it is extended to compute the denominator of the Jaccard index.
In addition to the (generalized) Jaccard index, this program offers a large variety of other (generalized) similarity measures, which may also be used to find item sets based on cover similarity, including the measures defined by by Kulczynski, Dice, Sokal & Sneath, Sokal & Michener, Faith, Rogers & Tanimoto etc. All of these measures can also be shown to be anti-monotone.
If you have trouble executing the program on Microsoft Windows, check whether you have the Microsoft Visual C++ Redistributable for Visual Studio 2017 (see under "Other Tools and Frameworks") installed, as the program was compiled with Microsoft Visual Studio 2017.
The algorithm used in this program is described in the following paper:
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.