Detecting smartphone user habits using sequential pattern analysis

Recently, the study of smart phone user habits has become a highly focused topic due to the rapid growth of the smart phone market. Indeed, sequential pattern analysis methods were efficiently used for web-based user habit mining long time ago. However, by means of simulations, it has been observed that these methods might fail for smart phone-based user habit mining. In this paper, we propose a novel approach that leads to a considerably increased performance of the traditional sequential pattern analysis methods by reasonably cutting off each chronological sequence of user logs on a device into shorter ones, which represent the sequential user activities in various periods of a day.