Density-based adaptive spatial clustering algorithm for identifying local high-density areas in georeferenced documents - Sakai, Tatsuhiro; Tamura, Keiichi; Kitakami, Hajime (Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics, 2014)
An emerging topic in social media is the increase in the number of geo-annotated documents, which include not only posted time but also posted location. Social media users have been transmitting information about things they witnessed themselves in their daily life through such geo-annotated (georeferenced) documents. Georeferenced documents are usually related to not only personal topics but also local topics and events. Therefore, identifying high-density areas associated with local "attractive" topics in georeferenced documents is one of the most important challenges in many application domains. In this study, we propose a novel density-based spatial clustering algorithm called the (ε,σ)- density-based adaptive spatial clustering algorithm for identifying high-density areas in which geo-related local topics in georeferenced documents are located. The (ε,σ)-density-based adaptive spatial clustering algorithm can identify local high-density areas by using adaptive spatial clustering criteria.