[Haskell-cafe] Announce: hlcm 0.2.2 - Parallel closed frequent itemsets mining

Alexandre Termier Alexandre.Termier at imag.fr
Wed Jun 16 09:11:40 EDT 2010

Dear all,

I'm pleased to announce the release of hlcm on Hackage :


hlcm is data mining tool for computing closed frequent itemsets.
This problem is famous as "market basket analysis":

   - given a list of transactions :
     [["bread", "butter","chocolate","tomato"]

   - and a minimal frequency threshold in [1..4], let's say 2: we want 
items that are sold together in at least 2 transactions

hlcm will tell you that ["bread","butter","chocolate"] appears in 3 
transactions and that ["bread","butter"] appears in 4 transactions.
You can many funnier applications with your own data, for example log 
analysis, mining words in web pages, etc.
You can see details on getting started with the program here: 
The library documentation is here : 

hlcm is based on the most efficient algorithm for closed frequent 
itemset mining, LCM, which is much, much faster than the well-known 
Apriori algorithm (more details when following the pointers from the 
homepage: http://membres-liglab.imag.fr/termier/HLCM/hlcm.html).

hlcm can also exploit parallelism through Strategies, with promising 
speedups. We still have more work to do in order to beat existing C/C++ 
implementations, but you can have a look at the paper that we submitted 
at Haskell Symposium this year for a detailed experimental study:
Don't miss out the section about the influence of RTS parameters on 
parallel performance.

Feel free to send me an e-mail if you have any question about hlcm.


Alexandre Termier
LIG (Laboratoire d'Informatique de Grenoble)
Université Joseph Fourier
681 rue de la Passerelle
B.P. 72, 38402 Saint Martin d'Hères (FRANCE)
Phone: +33 4 76 82 72 07
Fax: +33 4 76 82 72 87

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