TODMIS: Mining Communities from Trajectories
Existing algorithms for trajectory-based clustering usually rely on simplex representation and a single proximity-related distance (or similarity) measure. Consequently, additional information markers (e.g., social interactions or the semantics of the spatial layout) are usually ignored, leading to...
Saved in:
Main Authors: | LIU, Siyuan, WANG, Shuhui, JAYARAJAH, Kasthuri, MISRA, Archan, KRISHNAN, Rammaya |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2013
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/1958 https://ink.library.smu.edu.sg/context/sis_research/article/2957/viewcontent/TODMIS_pv_oa.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Discovering tactics in broadcast sports video with trajectories
by: Zhang, Y., et al.
Published: (2014) -
Can Instagram posts help characterize urban micro-events?
by: JAYARAJAH, Kasthuri, et al.
Published: (2016) -
Compressing Trajectory for Trajectory Indexing
by: Feng, Kaiyu, et al.
Published: (2018) -
COMPRESS: A comprehensive framework of trajectory compression in road networks
by: HAN, Yunheng, et al.
Published: (2017) -
Persistent Community Detection in Dynamic Social Networks
by: LIU, Siyuan, et al.
Published: (2014)