The week's pick
Reseach Article
Ensemble of Decision Tree Classifiers for Mining Web Data Streams
| Communications on Applied Electronics |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 1 - Number 1 |
| Year of Publication: 2014 |
| Authors: Fauzia Yasmeen Tani, Dewan Md. Farid, Mohammad Zahidur |
10.5120/65-0112
|
Fauzia Yasmeen Tani, Dewan Md. Farid, Mohammad Zahidur . Ensemble of Decision Tree Classifiers for Mining Web Data Streams. Communications on Applied Electronics. 1, 1 ( December 2014), 26-32. DOI=10.5120/65-0112
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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