Analyzing temporal event sequences play an important role in many application domains, such as workload behavior analysis, hardware fault diagnosis, and natural disaster resilience. As data volume keeps growing, real-world temporal event sequences are often noisy, missing, and complex, thus making it a daunting task to convey much of the information from a comprehensive overview for analysts. This work proposes a visual approach based on clustering and superimposing to construct a high-level overview of sequential event data while balancing the amount of information and its cardinality. We also implement an interactive prototype, called TimeRadar, that allows domain analysts to simultaneously analyze sequence clustering, extract distribution patterns, drill multiple levels of detail to accelerate the analysis. This work aims to provide an abstracted view of temporal event sequences where significant events are highlighted. The TimeRadar is demonstrated through case studies with real-world temporal datasets of various sizes.