Documents Classification System Using RapidMiner Tool

dc.contributor.advisor	Maher Abu Baker, Kamal Irshaid
dc.contributor.author	Abdulhaq, Razan
dc.contributor.author	Deriyieh, Rasha
dc.contributor.author	Adawi, Shorouq
dc.date.accessioned	2020-06-08T07:37:08Z
dc.date.available	2020-06-08T07:37:08Z
dc.date.issued	2019
dc.description.abstract	The exponential growth of the Internet has led to a great deal of interest especially by companies in developing useful and efficient Tools and Software’s to assist employees for doing their job and users for searching the web. However, the complexity of Natural Languages and the extremely High Dimensionality of the feature space of documents have made this Classification problem very difficult. We investigate four different Methods for Document Classification such as: the Naive Bayes classifier, the Nearest Neighbor Classifier, Decision Trees and a Support Vector Machine. These were applied to five classes of BBC and Reuters's news groups which is (Business, Entertainment, Politics, Sports and Technology) individually by using RapidMiner as a Tool. Our experimental results indicate that the Naive Bayes Classifier outperform the other classifiers on our data sets with a best accuracy of 85%. So we recommended companies to use RapidMiner as a Tool to classify their Documents and Naive Base as an algorithm to do this Classification.	en_US
dc.identifier.uri	https://hdl.handle.net/20.500.11888/15012
dc.title	Documents Classification System Using RapidMiner Tool	en_US
dc.type	Graduation Project	en_US