P-DBSCAN: A density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos
The rapid spread of location-based devices and cheap storage mechanisms, as well as fast development of Internet technology, allowed collection and distribution of huge amounts of user-generated data, such as people’s movement or geo-tagged photos. These types of data produce new challenges for research in different application domains. In many cases, new algorithms should be devised to better portray the phenomena under investigation. In this paper, we present P-DBSCAN, a new density-based clustering algorithm based on DBSCAN for analysis of places and events using a collection of geo-tagged photos. We thereby introduce two new concepts: (1) density threshold, which is defined according to the number of people in the neighborhood, and (2) adaptive density, which is used for fast convergence towards high density regions. Our approach is demonstrated on the area of Washington, D.C.
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