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Reseach Article

Ensemble of Decision Tree Classifiers for Mining Web Data Streams

by Fauzia Yasmeen Tani, Dewan Md. Farid, Mohammad Zahidur Rahman
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 2
Year of Publication: 2012
Authors: Fauzia Yasmeen Tani, Dewan Md. Farid, Mohammad Zahidur Rahman
10.5120/ijais12-450112

Fauzia Yasmeen Tani, Dewan Md. Farid, Mohammad Zahidur Rahman . Ensemble of Decision Tree Classifiers for Mining Web Data Streams. International Journal of Applied Information Systems. 1, 2 ( January 2012), 30-36. DOI=10.5120/ijais12-450112

@article{ 10.5120/ijais12-450112,
author = { Fauzia Yasmeen Tani, Dewan Md. Farid, Mohammad Zahidur Rahman },
title = { Ensemble of Decision Tree Classifiers for Mining Web Data Streams },
journal = { International Journal of Applied Information Systems },
issue_date = { January 2012 },
volume = { 1 },
number = { 2 },
month = { January },
year = { 2012 },
issn = { 2249-0868 },
pages = { 30-36 },
numpages = {9},
url = { https://www.ijais.org/archives/volume1/number2/65-0112/ },
doi = { 10.5120/ijais12-450112 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T10:41:06.722311+05:30
%A Fauzia Yasmeen Tani
%A Dewan Md. Farid
%A Mohammad Zahidur Rahman
%T Ensemble of Decision Tree Classifiers for Mining Web Data Streams
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 1
%N 2
%P 30-36
%D 2012
%I Foundation of Computer Science (FCS), NY, 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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Index Terms

Computer Science
Information Sciences

Keywords

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