The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
P-DBSCAN: a density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos
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.
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“Using geotagged data allows unstructured social media to be analysed and categorised numerically by using algorithms such as the density-based cluster algorithm proposed by Kisilevich et al. [34] to identify, for example, attractive places.”
From A Big Data Analytics Method for Tourist Behaviour Analysis (2017) · cites this paper as [34]
Citations
Cites
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Ankerst, M. (1999) · doi:10.1145/304181.304187 · Cited by 3,982
ST-DBSCAN: An algorithm for clustering spatial–temporal data
Birant, D., & Kut, A. (2006) · Data & Knowledge Engineering · doi:10.1016/j.datak.2006.01.013 · Cited by 1,437
An efficient approach to clustering in large multimedia databases with noise
Hinneburg, A., & Keim, D. A. (1998) · KOPS (University of Konstanz) · Cited by 1,164
Self-citationCrandall, D., Bäckström, L., Huttenlocher, D. P., & Kleinberg, J. M. (2009) · International World Wide Web Conference (WWW) · doi:10.1145/1526709.1526812 · Cited by 782
Citizens as Voluntary Sensors: Spatial Data Infrastructure in the World of Web 2.0
Goodchild, M. F. (2007) · Cited by 707
Digital Footprinting: Uncovering Tourists with User-Generated Content
Girardin, F., Calabrese, F., Fiore, F. D., et al. (2008) · IEEE Pervasive Computing · doi:10.1109/mprv.2008.71 · Cited by 470
A clustering-based approach for discovering interesting places in trajectories
Palma, A. T., Bogorny, V., Kuijpers, B., & Álvares, L. O. (2008) · ACM Symposium on Applied Computing · doi:10.1145/1363686.1363886 · Cited by 435
On Discovering Moving Clusters in Spatio-temporal Data
Kalnis, P., Mamoulis, N., & Bakiras, S. (2005) · Lecture notes in computer science · doi:10.1007/11535331_21 · Cited by 431
How flickr helps us make sense of the world
Kennedy, L. S., Naaman, M., Ahern, S., Nair, R., & Rattenbury, T. (2007) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1291233.1291384 · Cited by 386
Cited by
Sorted by citations
A Big Data Analytics Method for Tourist Behaviour Analysis
Miah, S. J., Vu, H. Q., Gammack, J., & McGrath, M. (2017) · Information & Management · doi:10.1016/j.im.2016.11.011 · Cited by 393
Metadata
- Authors
- Kisilevich, S., Mansmann, F., & Keim, D.
- Affiliations
- University of Konstanz (DE)
- ORCID
- Keim, Daniel
- Venue
- Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application
- Publisher
- ACM
- Published
- 2010-06-21
- Pages
- 1-4
- OpenAlex
- W1987995612
- Cited by
- 209
- References
- 19
- Citations by year
- 2025: 8 · 2024: 4 · 2023: 4 · 2022: 7 · 2021: 15 · 2020: 18 · 2019: 20 · 2018: 29 · 2017: 20 · 2016: 20 · 2015: 24 · 2014: 19 · 2013: 9 · 2012: 6
- FWCI
- 7.71
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference and Exhibition on Computing for Geospatial Research & Application (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Management and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- DBSCAN, density-based clustering, geotagged photos, place analysis, event analysis, density limit, user-generated data, popular movements
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- CoChat. (2026). Citation check: P-DBSCAN: a density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos. https://app.cochat.ai/papers/10.1145/1823854.1823897
- The paper
- Kisilevich, S., Mansmann, F., & Keim, D. (2010). P-DBSCAN: a density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos. Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application. https://doi.org/10.1145/1823854.1823897
