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This paper proposes a new approach for clustering English text documents, based on finding the pair wise correlation of documents in a given set of text documents. The correlation coefficient for each pair of documents is calculated on the basis of ranks given to the words in the documents. The ranking of the words occurring in a document is computed on the basis of weights of the words calculated...
We propose a fuzzy based method for multilabel text classification in which a document can belong to one or more than one category. In text categorization, the number of the involved features is usually huge, causing the curse of the dimensionality problem. Besides, a category can be a nonconvex region, which is a union of several overlapping or disjoint subregions. An automatic classification system,...
Bulk data is generated in the era of Information Technology. If it is not stored in a properly systematic manner then the generated data cannot be reused. This is because navigation becomes if not impossible, certainly very difficult. So we classify the data before it is stored. Present paper explores the techniques to store the data in a supervised classification paradigm using distributed features...
This paper presents computational methods aiming to patent's text categorization in Portuguese language, involving techniques from machine learning and computational linguistics. The algorithm used was the k-Nearest Neighbor method (k-NN) modified which showed good results, although it requires much computational time in the training stage. For the pre-processing step, it was implemented, with modifications,...
Text classification is the key technology for topic tracking, and vector space model (VSM) is one of the most simple and effective model for topics representation. On the basis of K-nearest neighbor (KNN) algorithm for text classification and support vector machines (SVM) algorithm for text classification, we have studied how they affect topic tracking. Then we get the variation law that they affect...
Text classification may be viewed as assigning texts in a predefined set of categories. However there are many digital documents that are not organized according to their contents. So it is difficult task to find relevant documents for a user. Automatic text classification problem can solve this problem. In this paper we introduce a new random walk term weighting method for improved text classification...
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