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

Text Classification and Distributional features techniques in Datamining and Warehousing