Now a days, the ‘Data Science Engineering’ becoming emerging trend to discover knowledge from web videos such as- YouTube videos, Yahoo Screen, Face Book videos etc. Petabytes of web video are being shared on social websites and are being used by the trillions of users all over the world. Recently, discovering outliers among large scale web videos have attracted attention of many web multimedia mining researchers. There are plenty of outliers abnormal video exists in different category of web videos. The task of classifying and prediction of web video as- normal and abnormal have gained vital research aspect in the area of Web Mining Research. Hence, we propose novel techniques to predict outliers from the web video dataset based on their metadata objects using data mining algorithms such as Random Tree (RT) and J48 Tree algorithms. The results of Decision Tree and J48 Tree classification models are analyzed and compared as a strategy in the process of knowledge discovery from web videos.

Outliers, Decision Tree, J48 Tree, Web Video Outliers, Prediction, Knowledge Discovery

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