Statistical data mining technique for salient feature extraction Online publication date: Thu, 18-Jul-2019
by Yeturu Jahnavi
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 18, No. 4, 2019
Abstract: Internet-based news documents are an important source of information transmission. Large numbers of news documents from various news wire sources are available on the internet. The objective of this work is to study the existing term weighting algorithms for feature extraction and to develop an efficient term weighting algorithm for mining salient features from internet-based newswire sources. TF*PDF is the influential algorithm that satisfies the basic property of the features in news documents, i.e., frequency and thus increases the accuracy when compared to other term weighing algorithms. However, only frequency property is not sufficient for salient topic extraction. To overcome that problem, this paper presents an innovative and effective term weighting algorithm that considers position, scattering and topicality along with frequency for extracting salient events. Experimental evaluation shows that the proposed term weighting algorithm performs better than the existing term weighting algorithms in terms of coverage rate.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
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 Intelligent Systems Technologies and Applications (IJISTA):
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 subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook