  We present a supervised learning algorithm for text categorization which has
brought the team of authors the 2nd place in the text categorization division
of the 2012 Cybersecurity Data Mining Competition (CDMC'2012) and a 3rd prize
overall. The algorithm is quite different from existing approaches in that it
is based on similarity search in the metric space of measure distributions on
the dictionary. At the preprocessing stage, given a labeled learning sample of
texts, we associate to every class label (document category) a point in the
space of question. Unlike it is usual in clustering, this point is not a
centroid of the category but rather an outlier, a uniform measure distribution
on a selection of domain-specific words. At the execution stage, an unlabeled
text is assigned a text category as defined by the closest labeled neighbour to
the point representing the frequency distribution of the words in the text. The
algorithm is both effective and efficient, as further confirmed by experiments
on the Reuters 21578 dataset.
