Microsaccades, small involuntary eye movements that occur once or twice per second during attempted visual fixation, are relevant to perception, cognition, and oculomotor control and present distinctive characteristics in visual and oculomotor pathologies. Thus, the development of robust and accurate microsaccade-detection techniques is important for basic and clinical neuroscience research. Due to the diminutive size of microsaccades, however, automatic and reliable detection can be difficult. Current challenges in microsaccade detection include reliance on set, arbitrary thresholds and lack of objective validation. Here we describe a novel microsaccade-detecting method, based on unsupervised clustering techniques, that does not require an arbitrary threshold and provides a detection reliability index. We validated the new clustering method using real and simulated eye-movement data. The clustering method reduced detection errors by 62% for binocular data and 78% for monocular data, when compared to standard contemporary microsaccade-detection techniques. Further, the clustering method's reliability index was correlated with the microsaccade-detection error rate, suggesting that the reliability index may be used to determine the comparative precision of eye-tracking devices.