Details
| Original language | English |
|---|---|
| Title of host publication | 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2013 |
| Pages | 510-513 |
| Number of pages | 4 |
| Publication status | Published - Nov 2013 |
| Event | 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2013 - Orlando, FL, United States Duration: 5 Nov 2013 → 8 Nov 2013 |
Publication series
| Name | GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems |
|---|
Abstract
In this paper we aim to recognize a priori unknown group movement patterns. We propose a constellation-based approach to extract repetitive relative movements of a constant group, which are allowed to be rotated, translated or differently scaled. To this end, we record a sequence of constellations, which are used for describing the movements relatively. We deal with uncertainties, and similarities of constellations respectively, by clustering the constellations. Further, we have developed a sequence mining algorithm, which uses the clustering results and tree-like data structures to extract the requested patterns from the sequence. Finally, this approach is applied to different datasets containing real trajectory data provided by different tracking devices. By this way, we want to show its portability to different use cases.
Keywords
- clustering, constellation, movement patterns, pattern mining, spatio-temporal analysis
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Earth-Surface Processes
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Modelling and Simulation
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
- Computer Science(all)
- Information Systems
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2013. 2013. p. 510-513 (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Mining group movement patterns
AU - Feuerhake, Udo
AU - Sester, Monika
PY - 2013/11
Y1 - 2013/11
N2 - In this paper we aim to recognize a priori unknown group movement patterns. We propose a constellation-based approach to extract repetitive relative movements of a constant group, which are allowed to be rotated, translated or differently scaled. To this end, we record a sequence of constellations, which are used for describing the movements relatively. We deal with uncertainties, and similarities of constellations respectively, by clustering the constellations. Further, we have developed a sequence mining algorithm, which uses the clustering results and tree-like data structures to extract the requested patterns from the sequence. Finally, this approach is applied to different datasets containing real trajectory data provided by different tracking devices. By this way, we want to show its portability to different use cases.
AB - In this paper we aim to recognize a priori unknown group movement patterns. We propose a constellation-based approach to extract repetitive relative movements of a constant group, which are allowed to be rotated, translated or differently scaled. To this end, we record a sequence of constellations, which are used for describing the movements relatively. We deal with uncertainties, and similarities of constellations respectively, by clustering the constellations. Further, we have developed a sequence mining algorithm, which uses the clustering results and tree-like data structures to extract the requested patterns from the sequence. Finally, this approach is applied to different datasets containing real trajectory data provided by different tracking devices. By this way, we want to show its portability to different use cases.
KW - clustering
KW - constellation
KW - movement patterns
KW - pattern mining
KW - spatio-temporal analysis
UR - http://www.scopus.com/inward/record.url?scp=84893494590&partnerID=8YFLogxK
U2 - 10.1145/2525314.2525318
DO - 10.1145/2525314.2525318
M3 - Conference contribution
AN - SCOPUS:84893494590
SN - 9781450325219
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 510
EP - 513
BT - 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2013
T2 - 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2013
Y2 - 5 November 2013 through 8 November 2013
ER -