This paper introduces a novel method to efficiently discover/cluster landmark images in large image collection. We consider each cluster as a combination of several sub-clusters, which is composed of images taken from different view points of the identical landmark. For each sub-cluster, we find its local centre represented by a group of similar images, and define it as the bundling centre (BC). We therefore start the image discovery/clustering by identifying the BCs and accomplish the task by efficiently growing and merging those sub-clusters represented by different BCs. In our proposed method, we use min-hash based method to build a sparse graph so as to avoid the time-consuming full-scale exhaustive pairwise image matching. Based on the information provided by the sparse graph, BCs are identified as local dense neighbors sharing high intra-similarity. We have also proposed a weighted voting method to efficiently grow these BCs with high accuracy. More importantly, the fixed local centres can ensure each sub-cluster contains identical landmark and generate result with high precision. In addition, compared to a single representative(iconic) image, the group of similar images obtained by each BC can provide more comprehensive cluster information and, thus, overcome the problem of low recall caused by information lost during visual word quantization. We present experimental results on two landmark datasets and show that, without query expansion, our method can boost landmark image discovery/clustering performances of state of the art techniques.