  Text Categorization is traditionally done by using the term frequency and
inverse document frequency.This type of method is not very good because, some
words which are not so important may appear in the document .The term frequency
of unimportant words may increase and document may be classified in the wrong
category.For reducing the error of classifying of documents in wrong category.
The Distributional features are introduced. In the Distribuional Features, the
Distribution of the words in the whole document is analyzed. Whole Document is
very closely analyzed for different measures like FirstAppearence, Last
Appearance, Centriod, Count, etc.The measures are calculated and they are used
in tf*idf equation and result is used in k- nearest neighbor and K-means
algorithm for classifying the documents.
