EFFICIENT MINING FOR SOCIAL NETWORKS USING INFORMATION GAIN RATIO BASED ON ACADEMIC DATASET

Studies on detecting the research progress in the academic research domain is challenging for research communities and funding agencies. The data retrieved from the social networks augment this issue for supporting the results in this direction. Here in this paper we address this issue positively with the help text mining tasks. Classification as one of the major data mining information gain ratio methodologies can be applied effectively for this purpose. The objective of this paper is to check the learning algorithms for classification such examples based on selected balanced dataset for research articles in technical conferences. The main intention in this context is to deal with available balanced data set for high accuracy. For this purpose various types of classifiers like Decision Trees, Rules, Naïve Bayes, and Meta learning models are built using an open source mining tool Weka. It is necessary to reduce the error before constructing the final models and thus the varying the parameters and number of iterations for training is carried out.