P-DBSCAN: A density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos

Slava Kisilevich, Florian Mansmann, Daniel Keim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

108 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationCOM.Geo 2010 - 1st International Conference and Exhibition on Computing for Geospatial Research and Application
DOIs
StatePublished - 6 Aug 2010
Externally publishedYes
Event1st International Conference and Exhibition on Computing for Geospatial Research and Application, COM.Geo 2010 - Washington, DC, United States
Duration: 21 Jun 201023 Jun 2010

Publication series

NameACM International Conference Proceeding Series

Conference

Conference1st International Conference and Exhibition on Computing for Geospatial Research and Application, COM.Geo 2010
Country/TerritoryUnited States
CityWashington, DC
Period21/06/1023/06/10

Keywords

  • Attractive places
  • Density based clustering
  • Geo-tagged photos

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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