A novel approach for effective web page classification Online publication date: Tue, 29-Jul-2014
by J. Alamelu Mangai; V. Santhosh Kumar; S. Appavu Balamurugan
International Journal of Data Mining, Modelling and Management (IJDMMM), Vol. 5, No. 3, 2013
Abstract: With the exponential increase in volume of the WWW every day, web page classification has become tedious. Since with no quality data there is no quality mining results, it is worth to emphasise on fine tuning the data for classification, rather than improving the classifiers themselves. This paper investigates the methods for improving web page classification by feature extraction, selection and data tuning. This paper also proposes a new classification model for web page classification called a probabilistic web page classifier (PWPC). It is based on a probabilistic framework and attribute-value similarity measure (AVS). The proposed method is tested on a benchmarking dataset, WebKB and the performance of PWPC on the fine tuned web pages has exhibited significant accuracy over the traditional machine learning classifiers.
Online publication date: Tue, 29-Jul-2014
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Mining, Modelling and Management (IJDMMM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org