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**11 - 14**of**14**### Industry/Government Track Poster 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 mixture of multinomial models, has likewise been successfully applied to text clustering problems. Despite the strong independence assumptions that these models make, their attractiv ..."

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Naive Bayes classifier has long been used for text categorization tasks. Its sibling from the unsupervised world, the 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, as well as ability to handle heterogeneous features and multiple classes. Recently, there has been several attempts to improve the accuracy 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.

### Member

, 2003

"... Various applications of information theoretical and combinatorial methods in data mining are presented. An axiomatization has been introduced for a family of entropies including both Shannon entropy and the Gini index as special cases. These entropies, and distances based on them, were then applied ..."

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Various applications of information theoretical and combinatorial methods in data mining are presented. An axiomatization has been introduced for a family of entropies including both Shannon entropy and the Gini index as special cases. These entropies, and distances based on them, were then applied to decision tree construction. It has been shown experimentally that trees using distances based on generalized entropies as splitting criteria are smaller than those constructed using other criteria without significant loss in accuracy. One of the major problems in association rule mining is the huge number of rules produced. This work contains contributions to two principal methods of addressing the problem: sorting rules based on some interestingness measure, and rule pruning. A new measure of rule interestingness is introduced generalizing three well-known measures: chi-squared, entropy gain and Gini gain, which moreover gives a whole family of intermediate measures with interesting properties. Also, iv a method of pruning association rules using the Maximum Entropy Principle has

### AN ITERATIVE PROCEDURE FOR GENERAL PROBABILITY MEASURES TO OBTAIN I-PROJECTIONS ONTO INTERSECTIONS OF CONVEX SETS

, 2006

"... The iterative proportional fitting procedure (IPFP) was introduced formally by Deming and Stephan in 1940. For bivariate densities, this procedure has been investigated by Kullback and Rüschendorf. It is well known that the IPFP is a sequence of successive I-projections onto sets of probability meas ..."

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The iterative proportional fitting procedure (IPFP) was introduced formally by Deming and Stephan in 1940. For bivariate densities, this procedure has been investigated by Kullback and Rüschendorf. It is well known that the IPFP is a sequence of successive I-projections onto sets of probability measures with fixed marginals. However, when finding the I-projection onto the intersection of arbitrary closed, convex sets (e.g., marginal stochastic orders), a sequence of successive I-projections onto these sets may not lead to the actual solution. Addressing this situation, we present a new iterative I-projection algorithm. Under reasonable assumptions and using tools from Fenchel duality, convergence of this algorithm to the true solution is shown. The cases of infinite dimensional IPFP and marginal stochastic orders are worked out in this context. 1. Introduction. For two probability measures (PM) P and Q defined on an arbitrary measurable space (X,B), the I-divergence or the Kullback–