We propose an appearance-based image clustering approach called GGCI (global geometric clustering for image). For face images taken with varying pose, expression, eyes (wearing sunglasses or not) or object images under different viewing conditions, GGCI uses easily measured local metric information to learn the underlying global geometry of images space, then apply the extended nearest neighbor approach to cluster images. Different from the usual nearest neighbor approach, GGCI considers the density around the nearest points within clusters. Moreover, our approach clusters based on the geodesic distance measure instead of Euclidean distance measure, which better reflects the intrinsic geometric structure of manifold embedded in high dimensional image space. Experimental results suggest that the proposed GGCI approach achieves lower error rates in image clustering when manifolds are embedded in image space.