A new clustering framework uses a-contrario anomaly detection to define clusters as maximal subsets without anomalies under a null hypothesis of randomness. The Perception algorithm identifies outliers using an expectation-based threshold (\mathbb{E} < 1), enabling robust, parameter-free clustering that expands from minimal seed inputs and handles noise and emerging clusters effectively.