SCAN enhances reconstruction-based time series anomaly detection by integrating multi-scale neighborhood-centered clustering. It uses cluster center representations to constrain normal pattern reconstruction and derives an anomaly confidence score based on cluster membership probability, combined with reconstruction error. Extensive experiments on real-world datasets show SCAN achieves state-of-the-art performance.