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Ensemble of Decision Tree Classifiers for Mining Web Data Streams

Fauzia Yasmeen Tani, Dewan Md. Farid, Mohammad Zahidur Rahman Published in Information Systems

Communications on Applied Electronics
Year of Publication 2014
© 2014 by CAE Journal
10.5120/65-0112
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Fauzia Yasmeen Tani, Dewan Md. Farid and Mohammad Zahidur. Ensemble of Decision Tree Classifiers for Mining Web Data Streams. Communications on Applied Electronics 1(1):26-32, 2014. Published by Foundation of Computer Science, New York, USA. BibTeX

@article{key:article,
	author = {Fauzia Yasmeen Tani and Dewan Md. Farid and Mohammad Zahidur},
	title = {Ensemble of Decision Tree Classifiers for Mining Web Data Streams},
	journal = {Communications on Applied Electronics},
	year = {2014},
	volume = {1},
	number = {1},
	pages = {26-32},
	note = {Published by Foundation of Computer Science, New York, USA}
}

Abstract

The World Wide Web (www or w3 commonly known as the web) is the largest database available with growth at the rate of millions of pages a day and presents a challenging task for mining web data streams. Currently extraction of knowledge from web data streams is getting more and more complex, because the structure of data doesn’t match the attribute-values when considering the large volume of web data. In this paper, an ensemble of decision tree classifiers is presented, which is an efficient mining method to obtain a proper set of rules for extracting knowledge from a large amount of web data streams. We built a web server using Model 2 Architecture to collect the web data streams and applied the ensemble classifier for generating decision rules using several decision tree learning models. Experimental results demonstrate that the proposed method performs well in decision making and predicting the class value of new web data streams.

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Keywords

Data Streams, Decision Tree, J2EE, Model 2 Architecture, Web Server, and Web Mining