Applying data mining techniques to explore user behaviors and watching video patterns in converged IT environments

Abstract

Comfortable leisure and entertainment is expected through multimedia. Web multimedia systems provide diversified multimedia interactions, for example, sharing knowledge, experience and information, and establishing common watching habits. People use information technology (IT) systems to watch multimedia videos and to perform interactive functions. Moreover, IT systems enhance multimedia interactions between users. To explore user behaviors in viewing multimedia videos by key points in time, multimedia video watching patterns are analyzed by data mining techniques. Data mining methods were used to analyze users' video watching patterns in converged IT environments. After the experiment, we recorded the processes of clicking the Web multimedia video player. The system logs of using the video player are classified into four variables, playing time, active playing time, played amount, and actively played amount. To explore the four variables, we apply the k-means clustering technique to organize the similar playing behavior patterns of the users into three categories: actively engaged users, watching engaged users, and long engaged users. Finally, we applied statistical analysis methods to compare the three categories of users' watching behaviors. The results showed that there were significant differences among the three categories.