i noticed that the mahout encoding of text documents in a k-means example uses TF-IDF and then converts the documents into vectors (so one vector per document) where every word gets mapped to an int for the vector index and the words TFIDF score becomes the vector value for that index. it also creates a dictionary to be able to get back from the index to the word. did i get that right?
why this approach as opposed to the feature hashing that i saw in other places in mahout? thanks! koert
