First of all, I hope I'm in the right StackExchange here. If not, apologies!
I'm currently working with huge amounts of feature-value vectors. There are millions of these vectors (up to 20 million possibly). They contain some linguistic/syntactic features and their values are all strings.
Because most classifiers do not handle string data as values, I convert them to binary frequency values, so an attribute looks like this:
@attribute 'feature#value' numeric
And per row, the value is either 1 or it is absent (so note it's a sparse ARFF file).
The thing is, with 250K rows, there are over 500K attributes and so, most algorithms have a hard time with this.
There are a lot of algorithms. I'm really curious as to what you would consider a suitable one (preferably unsupervised, but anything works), and if you even have some ideas how I could improve performance. I can train on small subsets of data, but the results only get better when using large amounts of data (at least 7 million events).
For now, I've been using NaiveBayes variations (Multinomial and also DMNBText) and those are really the only ones that are able to chew up data with acceptable speed.
Thanks a lot. If you need more information, please let me know.