document: Document preprocessing for naive Bayes classification and clustering with mixture of multinomials Naive Bayes classifier has long been used for text categorization tasks . Its sibling from the unsupervised world , the probabilistic mixture of multinomial models , has likewise been successfully applied to text clustering problems . Despite the strong independence assumptions that these models make , their attractiveness come from low computational cost , relatively low memory consumption , ability to handle heterogeneous features and multiple classes , and often competitiveness with the top of the line models . Recently , there has been several attempts to alleviate the problems of Naive Bayes by performing heuristic feature transformations , such as IDF , normalization by the length of the documents and taking the logarithms of the counts . We justify the use of these techniques and apply them to two problems : classification of products in Yahoo ! Shopping and clustering the vectors of collocated terms in user queries to Yahoo ! Search . The experimental evaluation allows us to draw conclusions about the promise that these transformations carry with regard to alleviating the strong assumptions of the multinomial model .
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