I am working on a project called "association rule discovery from social network data: Introducing Data Mining to the Semantic Web". Can anyone suggest a good source for an algorithm (and its code. I heard that it can be implemented using Perl and also R packages) to find association rules from a social network database?
The snapshot of the database can be got in the following link: https://docs.google.com/uc?id=0B0mXGRdRowo1MDZlY2Q0NDYtYjlhMi00MmNjLWFiMWEtOGQ0MjA3NjUyZTE5&export=download&hl=en_US
The dataset is available on the following link: http://ebiquity.umbc.edu/get/a/resource/82.zip
I have searched a lot regarding this project but unfortunately can't find something useful as yet. The following link I found somewhat related:
Criminal data : http://www.computer.org/portal/web/csdl/doi/10.1109/CSE.2009.435
Your help will be highly appreciated.
Thank You,
This is a bit broader than http://en.wikipedia.org/wiki/Association_rule_learning but hopefully useful.
Some earlier FOAF work that might be interesting (SVD/PCA etc):
http://stderr.org/~elw/foaf/ http://www.scribd.com/doc/353326/The-Social-Semantics-of-LiveJournal-FOAF-Structure-and-Change-from-2004-to-2005 http://datamining.sztaki.hu/files/snakdd.pdf
Also Ch.4 of http://www.amazon.com/Understanding-Complex-Datasets-Decompositions-Knowledge/dp/1584888326 is devoted to the application of matrix decomposition techniques against graph data structures; strongly recommended.
Finally, Apache Mahout is the natural choice for large scale data mining, machine learning etc., https://cwiki.apache.org/MAHOUT/dimensional-reduction.html