An dual function distance metric for pattern matching based hotspot clustering is described. The dual function distance metric can handle patterns containing multiple polygons, is easy to compute, and is tolerant of small variations or shifts of the shapes. Compared with an XOR distance metric pattern clustering, the dual function distance metric can achieve up to 37.5% accuracy improvement with 2X-4X computational cost in the context of cluster analysis. The dual function distance metric is reliable and accurate for characterizing clips (e.g. hotspots), thereby making it desirable for industry applications.