Sleep behavior is traditionally monitored with polysomnography, and sleep stage patterns are a key marker for sleep quality used to detect anomalies and diagnose diseases. With the growing demand for personalized healthcare and the prevalence of the Internet of Things, there is a trend to use everyday technologies for sleep behavior analysis at home, having the potential to eliminate expensive in-hospital monitoring. In this paper, we conceived a multi-source data mining approach to personalized sleep-wake pattern recognition which uses physiological data and personal information to facilitate fine-grained detection. Physiological data includes actigraphy and heart rate variability and personal data makes use of gender, health status, and race information which are known influence factors. Moreover, we developed a personalized sleep parameter extraction technique fused with the sleep-wake approach, achieving personalized instead of static thresholds for decisionmaking. Results show that the proposed approach improves the accuracy of sleep and wake stage recognition, therefore offering a new solution for personalized sleep-based health monitoring.