4.1. Feature extraction
The sklearn.feature_extraction module can be used to extract features in a format supported by machine learning algorithms from datasets consisting of formats such as text and image.
Note
Feature extraction is very different from Feature selection: the former consists in transforming arbitrary data, such as text or images, into numerical features usable for machine learning. The latter is a machine learning technique applied on these features.
http://scikit-learn.org/stable/modules/feature_extraction.html
What do you mean by "aggregation"? May be the tools like RapidMiner would be useful for you?
http://www.youtube.com/watch?v=EjD2M4r4mBM&list=PL7669FFBBA1825900&index=2
Thanks Sir. Andrey but not what I'm looking for, i give you an example:
for numerical operation : aggregation =(5+8+4+3) / 4=5
for textual aggregation = (TCP + adress + IP + wifi) = network
other example = (data set + olap + cube + ...) = data warehousing
i proposed two approaches based on keywords for automatic textual aggregation that will be published soon
It seems to be the hard task, cause I don't know any way to formally define your "textual aggregation"- operation. To start, I see two possibilities. The first is to define it a priory by the set of the rules, but it looks like hard-coding and isn't agile. The second is to use AI-methods to derive some rules from texts. Or my be something third...
It's interesting, which one you have chosen?
it's easy task if i have the ontology of domain to select the last common ancentre, but if i don't have it, i will propos i new approach that select the most represntative keywords in the corpus
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