Feature Selection I've a facebook users dataset in which each user has a "huge" set of attribute, i.e about 220 attributes like age, hometown, religion, and a set of facebook liked pages to store the users tastes.
Now I need an automatic tool (i've no time to develop an entire framework) to select only the relevant features in the dataset, in order to make agile the cluster algorithms that i've to use.
My only constraint is that I need a unsupervised features selection technique, since I've no further information than my initial dataset.
Any help?
 A: To select the important variables (features) in your dataset, you could perform variable clustering.
In variable clustering, the correlation between the variables is of interest (you want to cluster variables that are highly correlated with each other). This R package may provide more insight into how it works and will actually implement a method for you to use.
One way of performing variable clustering that may provide some intuition into your problem is to perform PCA on your data and then cluster variables that are grouped together by the PCA loadings. In a sense, this is what the methods in that package do. Intuitively, one could say that they are highly correlated with the same dimension of the PCA subspace and therefore with each other - thus they should be clustered. Note that before performing PCA and other similar procedures, one should standardize the variables.
In the comments below, you express that this approach may not be automatic enough for your specific situation. You claim that you need to run variable selection on multiple datasets and aggregate the findings. An alternative with a more automatic selection could be to use sparse PCA (PCA-based methods should probably be acceptable since the variable clustering also uses PCA). You will need to select a level of "sparsity" (how many variables to exclude). 
If you can run Matlab, the simplest thing would probably be to use the package by Karl Sjorstrand and run spca using soft thresholding (see example in the function) to select the level of sparsity automatically. Do this for all of your subsets and then aggregate the results in a logical way (one could be to exclude variables that are always excluded, or excluded in almost all datasets). If you do not have Matlab, you can run spca in R using the elasticnet package, though I do not think it will automatically determine the sparsity parameters. You may find another R package that does though.
A: You can refer to the paper by P. Mitra, C. A. Murthy and S. K. Pal, named "Unsupervised Feature Selection using Feature Similarity, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol .24, No. 4, pp 301-312, April 2002". The code is available at http://cse.iitkgp.ac.in/~pabitra/paper/fsfs.tar.gz. The paper can be found here.
A: I reply to me and to those of you who are interested, I tried a simple matlab package SPEC, for (spectral) feature selection.
You can find the paper here and the matlab code here.
The algorithm doesn't select automatically the relevant features, but it weighs the features according to their consistency in the dataset (low weight means high relevance), see the paper for more details.
