Title :
Detecting expectation-based spatio-temporal clusters formed during opportunistic sensing
Author :
Orlinski, Matthew ; Filer, Nick
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;
Conference_Titel :
Pervasive Computing and Communications Workshops (PERCOM Workshops), 2014 IEEE International Conference on
Conference_Location :
Budapest
DOI :
10.1109/PerComW.2014.6815271
