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Implementation of Web Content Mining by Using Bayesian Classifier
http://hdl.handle.net/20.500.12678/0000003962
http://hdl.handle.net/20.500.12678/00000039621dca6fb7-5300-4b05-ba08-97fc2ab371dc
a6327686-e581-4282-b72b-411069550c45
Publication type | ||||||
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Article | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Implementation of Web Content Mining by Using Bayesian Classifier | |||||
Language | en | |||||
Publication date | 2009-12-30 | |||||
Authors | ||||||
Yu, Hnin Myat | ||||||
Thein, Naychi Lai Lai | ||||||
Description | ||||||
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 usedthese technique minimize the set of resulting pages tothe user when searching and show the users whatinformation is available | ||||||
Keywords | ||||||
news genre classification, web content mining, Bayes Theorem | ||||||
Identifier | http://onlineresource.ucsy.edu.mm/handle/123456789/1678 | |||||
Journal articles | ||||||
Fourth Local Conference on Parallel and Soft Computing | ||||||
Conference papers | ||||||
Books/reports/chapters | ||||||
Thesis/dissertations |