An approach to Re-evaluate CERS method for News Mining (International Journal of Engineering and Computer Science, 2014)
With the current growth rate of URLs we are at the age of online information overload and for many other domains such as web services data analysis. Text mining has been a key research topic for online information retrieval and information extraction. From online news and blog articles a human can often deduce information and knowledge for the prediction of market movements and other interesting activities occurring all around the world. However this recognition and comprehension process is very complex and requires some context knowledge about the domain in which trends are to detect.  The analysis of news source represents an important challenge of our times. News not only reflects the different processes happening in the world but also influences the economic, political and social situation. A news source contains an enormous amount of information which can be compiled together and analyzed. In this paper we proposed an approach that applies clustering methods on news articles and then CERS (Cross Entropy Reduction Sampling) technique to make a news article more effective to search and less cumbersome to get exact knowledge.