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real data example (normalised doc lengths)

this time let's so the same but include a really simple normalisation; divide each term's weight based on the document length.

when we do this we get an immediate improvement on the spread for the first feature.
like last time it consists of english construct words but this time isn't dominated by autoblog articles

feature 1 article strengths
(articles near top most strongly associated)
without normalisation with normalisation
autoblog the register perez hilton

and like last time the following few features (f2 to f6) are related to single documents which have some fundamental difference in them to the entire corpus

features 7 through 10 show an interesting seperation of the documents
consider especially f8 vs f9

feature 7 to feature 9 scatterplot matrix
autoblog the register perez hilton

here's an undirected 2d tour of the feature space for features 7 through 10, seems to be quite a bit of seperation.

feature 7 to feature 9 scatterplot matrix
autoblog the register perez hilton

so finally, some conclusions

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