Analytical Review of the News Data Classification Methods with Multivariate Classification Attributes

-The new classification has been emerged as the important sub-branch of the data mining. A lot of work has been already done on the news classification with variety of classifiers and feature descriptors. A number of news classification projects are working on the real-time systems in existence today. The news classification is the important part of the online news portals. The online news portals are rising every year, and adding more users to the news portals. The news classification is the branch of text classification or text mining. The researchers have already done a lot of work on the text classification models with different approaches. The news works has to be classified in the form of various categories such as sports, political, technology, business, science, health, regional and many other similar categories. The researchers have already worked with many supervised and unsupervised methods for the purpose of news classification. The supervised models have been found more efficient for the purpose of news classification. The major goal of the news classification research is to improve the accuracy while decreasing the elapsed time. Our news classification models purposes the use of k-means and lexicon analysis of the news data with nearest neighbor algorithm for the news classification. The k-means algorithm is the clustering algorithm and used primarily to produce the text data clusters with the important information. Then the lexicon analysis would be performed over the given text data and then final classification of the news is done using k-nearest neighbor. The results would be obtained in the form of the parameters of accuracy, elapsed time, etc.