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Classification of Publication Papers by Using K-nearest Neighbor Algorithm

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dc.contributor.author Tun, May Zin
dc.date.accessioned 2019-08-06T00:56:42Z
dc.date.available 2019-08-06T00:56:42Z
dc.date.issued 2009-12-30
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1812
dc.description.abstract Classification is a data mining or machine learning technique used to predict group membership for data instances. Several major kinds of classification method including decision tree induction method, Bayesian networks method, k-nearest neighbor classification method, case-based reasoning, genetic algorithm and fuzzy logic techniques. Classification is the task of deciding whether a paper belongs to a set of pre-specified classes of papers. Automatic classification schemes can greatly facilitate the process of categorization. Categorization of documents is challenging, as the number of discriminating words can be very large. In this paper, we presented categorization of publication papers by applying k-nearest neighbor classification using the Euclidean Distance measure.K-nearest neighbor method is the simplest and most straightforward method among all classification methods. Hence, knearest neighbor method is used to classify different number of nearest neighbors for different categories, rather than a fixed number across all categories in this system.This system is intended to classify different categories from different papers in data sets and to save time for searching papers. en_US
dc.language.iso en en_US
dc.publisher Fourth Local Conference on Parallel and Soft Computing en_US
dc.subject Text Categorization en_US
dc.subject Data Mining en_US
dc.subject Classification en_US
dc.title Classification of Publication Papers by Using K-nearest Neighbor Algorithm en_US
dc.type Article en_US


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