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Implementation of Web Content Mining by Using Bayesian Classifier

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dc.contributor.author Yu, Hnin Myat
dc.contributor.author Thein, Naychi Lai Lai
dc.date.accessioned 2019-08-03T01:07:50Z
dc.date.available 2019-08-03T01:07:50Z
dc.date.issued 2009-12-30
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1678
dc.description.abstract The web is a huge repository of information and there is a need for categorizing web documents to facilitate the search and retrieval of pages. Existing algorithms rely solely on the text content of the web pages for classification. In text and web page classification, Bayesian prior probabilities are usually based on term frequencies, term counts within a page. This paper presented a Naïve Bayes web page classification system to classify news genres .The features of web news genres are represented as vector representations using TF*IDF functions. For classification, there are two step; first is extracting the features from the web page and second is based on the training set by using Bayes Theorem to determine the categories of unknown web pages such as arts, health and so on. The system used these technique minimize the set of resulting pages to the user when searching and show the users what information is available en_US
dc.language.iso en en_US
dc.publisher Fourth Local Conference on Parallel and Soft Computing en_US
dc.subject news genre classification en_US
dc.subject web content mining en_US
dc.subject Bayes Theorem en_US
dc.title Implementation of Web Content Mining by Using Bayesian Classifier en_US
dc.type Article en_US

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