Most recently the social media sharing websites such as Flickr, Facebook, and Picasa have allowed users to share their personal photos with friends. Moreover, people like to follow, forward their favorite images, which is one of the main source of near duplicate images. And also, the worldwide place of interests such as Roma, Statue of Liberty and London Tower Bridge etc., attract world-wide visitors. For these places, travelers take photos, write travelogues and share them with their social friends. The photos taken from the same place with or without viewpoint variations are near duplicate images. How to detect them is an ad-hoc problem in the area of image analysis and multimedia processing. The existing near duplicate image processing approaches mainly focused on finding the near duplicate images for a given input image, where a query image is needed. However, how to find the near duplicate image groups (NDIG) automatically from the web-scale social images is very challenging. So, in this paper, instead of searching near duplicates image for certain input image, we proposed an automatic NDIG mining approach by utilizing adaptive global feature clustering and local feature refinement. The proposed NDIG mining approach is achieved by utilizing a hierarchical model. It is a two-layer hierarchical structure by first utilizing adaptive global feature clustering based candidate NDIG detection and then using local feature refinement based NDIG verification. The global clustering is mainly for reducing computational cost for processing the large scale image set. The local refinement is for improving NDIG detection performances. Experiments on four datasets show the effectiveness of our approach.