Cognitive correlation of source-destination pair in a video conference network using call attributes

Abstract

The paper proposes cognitive learning technique for predicting the destination in a video conference being held over an organizational network. The dataset comprised of 22801 connectivity records of video conferences held during the year 2010-2013. Naive Bayes, k-NN and decision tree were trained on the dataset and the performance of the learning algorithms were evaluated. The destination has been predicted with an accuracy of 58.8% over the entire dataset and with 60.1% accuracy over a subset of the dataset. The results indicated deviation from machine learning trends and some of the reasons for deviations have been analyzed and presented while a few had been left out as research problem. There is scope for application of the presented learning technique in the areas of network anomaly detection, network visualization and connectivity prediction.