Density-based adaptive spatial clustering algorithm for identifying local high-density areas in georeferenced documents

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 (georef...

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Veröffentlicht in:Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics S. 513 - 518
Hauptverfasser: Sakai, Tatsuhiro, Tamura, Keiichi, Kitakami, Hajime
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.10.2014
Schlagworte:
adaptive spatial clustering
Algorithm design and analysis
Clustering algorithms
Data mining
DBSCAN
density-based cluster
Kernel
local topic extraction
Media
social media
spatial cluster
Spatiotemporal phenomena
Twitter
ISSN:1062-922X
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Zusammenfassung: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.
ISSN:1062-922X
DOI:10.1109/SMC.2014.6973959