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According to the popular use of digital camera, a traveler group carries multiple cameras for the same event. Previous studies on digital photo focused on massive unrelated photos or collections of a private user. But group travelers can carry several cameras and take photos simultaneously at the same time. Thus, an effective management for group photos is main issue for a traveler group. Since group photos from different photographers share their content, people need to collate the photos and classify them. We propose several supervised and unsupervised clustering methods for group photos. Previous studies are not applicable to group photos, because group photos do not guarantee clear relevance between photos which shown in private photo album. The proposed supervised clustering method, using spatio-temporal similarity, obtains a true cluster set of a specific camera from a user. It extracts discriminating features from given clusters and applies them to cluster other photos. Unsupervised methods use temporal photo blocks to compensate spatial variation of photos from a user. We use hierarchical clustering and neural network based clustering. In experiments, we show clustering results from real nomadic photo data. People can use a method suited to their needs.