DocumentCode
139749
Title
Detecting expectation-based spatio-temporal clusters formed during opportunistic sensing
Author
Orlinski, Matthew ; Filer, Nick
fYear
2014
fDate
24-28 March 2014
Firstpage
581
Lastpage
586
Abstract
Detecting clusters in the encounter graphs generated from reality mining data is one way of detecting the social and spatial relationships of participants. However, many of the existing clustering algorithms do not factor in the time since encounters, and can only be used to describe a single aggregated snapshot of the data. This paper describes a spatio-temporal clustering technique which has been used to reveal the transient communities within the data.
Keywords
data mining; pattern clustering; spatiotemporal phenomena; statistical analysis; clustering algorithms; data mining; opportunistic sensing; spatiotemporal cluster detection; spatiotemporal clustering technique; Clustering algorithms; Communities; Conferences; Data mining; Image edge detection; Measurement; Meetings;
fLanguage
English
Publisher
ieee
Conference_Titel
Pervasive Computing and Communications Workshops (PERCOM Workshops), 2014 IEEE International Conference on
Conference_Location
Budapest
Type
conf
DOI
10.1109/PerComW.2014.6815271
Filename
6815271
Link To Document