|32 bit||64 bit||(32/64 bit only for executable)|
|carpenter||(293 kb)||carpenter||(308 kb)||GNU/Linux executable|
|carpenter.exe||(178 kb)||carpenter.exe||(211 kb)||Windows console executable|
|carpenter.zip||(186 kb)||carpenter.tar.gz||(167 kb)||C sources, version 3.19 (2017.03.24)|
|census.zip||(382 kb)||census data set (UCI ML repository)|
|census||(2 kb)||shell script used for the conversion|
Carpenter is a program to find closed frequent item sets with the Carpenter algorithm [Pan et al. 2003], which enumerates transaction sets, in contrast to many other frequent item set mining algorithms, which enumerate item sets. Such an approach can be highly competitive in special cases, namely if there are few transactions and (very) many items, which is a common situation in biological data sets like gene expression data. For other data sets (fewer items, many transactions), however, it is not a recommendable approach.
By default the program finds closed item sets. It can also find maximal item sets, but the filtering of the closed item sets may not be very efficient.
This implementation offers a variant based on transaction identifier lists according to the description in [Pan et al. 2003], although with several optimizations due to which it significantly outperforms the implementation of the Gemini package, which is provided by the authors of [Pan et al. 2003].
The alternative is a variant based on an item occurrence counter table, which bears some vague resemblance to the horizontal approach in the RERII algorithm [Cong et al. 2004]. Which of the two variants is faster depends on the data set. By default, the variant to be used is chosen automatically based on the table size (essentially: if the data table fits into the processor cache, use the table, otherwise use the transaction identifier list version).
Full description of the Carpenter 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.
The improved Carpenter algorithm used in this program is described in the following paper:
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.