Detecting expectation-based spatio-temporal clusters formed during opportunistic sensing

Matthew Orlinski; Nick Filer

doi:10.1109/PerComW.2014.6815271

Detecting expectation-based spatio-temporal clusters formed during opportunistic sensing

Matthew Orlinski, Nick Filer

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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. © 2014 IEEE.

Original language	English
Title of host publication	2014 IEEE International Conference on Pervasive Computing and Communication Workshops, PERCOM WORKSHOPS 2014\|IEEE Int. Conf. Pervasive Comput. Commun. Workshops, PERCOM WORKSHOPS
Publisher	IEEE Computer Society
Pages	581-586
Number of pages	5
DOIs	https://doi.org/10.1109/PerComW.2014.6815271
Publication status	Published - 2014
Event	2014 IEEE International Conference on Pervasive Computing and Communication Workshops, PERCOM WORKSHOPS 2014 - Budapest Duration: 1 Jul 2014 → … http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6815271&isnumber=6815123

Conference

Conference	2014 IEEE International Conference on Pervasive Computing and Communication Workshops, PERCOM WORKSHOPS 2014
City	Budapest
Period	1/07/14 → …
Internet address	http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6815271&isnumber=6815123

Keywords

data mining
pattern clustering
spatiotemporal phenomena
statistical analysis
clustering algorithms
opportunistic sensing
spatiotemporal cluster detection
spatiotemporal clustering technique

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10.1109/PerComW.2014.6815271

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Orlinski, M., & Filer, N. (2014). Detecting expectation-based spatio-temporal clusters formed during opportunistic sensing. In 2014 IEEE International Conference on Pervasive Computing and Communication Workshops, PERCOM WORKSHOPS 2014|IEEE Int. Conf. Pervasive Comput. Commun. Workshops, PERCOM WORKSHOPS (pp. 581-586). IEEE Computer Society . https://doi.org/10.1109/PerComW.2014.6815271

@inproceedings{eed54a704eeb43638864f8339ceda875,

title = "Detecting expectation-based spatio-temporal clusters formed during opportunistic sensing",

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. {\textcopyright} 2014 IEEE.",

keywords = "data mining, pattern clustering, spatiotemporal phenomena, statistical analysis, clustering algorithms, opportunistic sensing, spatiotemporal cluster detection, spatiotemporal clustering technique",

author = "Matthew Orlinski and Nick Filer",

year = "2014",

doi = "10.1109/PerComW.2014.6815271",

language = "English",

pages = "581--586",

booktitle = "2014 IEEE International Conference on Pervasive Computing and Communication Workshops, PERCOM WORKSHOPS 2014|IEEE Int. Conf. Pervasive Comput. Commun. Workshops, PERCOM WORKSHOPS",

publisher = "IEEE Computer Society ",

address = "United States",

note = "2014 IEEE International Conference on Pervasive Computing and Communication Workshops, PERCOM WORKSHOPS 2014 ; Conference date: 01-07-2014",

url = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6815271&isnumber=6815123",

}

Orlinski, M & Filer, N 2014, Detecting expectation-based spatio-temporal clusters formed during opportunistic sensing. in 2014 IEEE International Conference on Pervasive Computing and Communication Workshops, PERCOM WORKSHOPS 2014|IEEE Int. Conf. Pervasive Comput. Commun. Workshops, PERCOM WORKSHOPS. IEEE Computer Society , pp. 581-586, 2014 IEEE International Conference on Pervasive Computing and Communication Workshops, PERCOM WORKSHOPS 2014, Budapest, 1/07/14. https://doi.org/10.1109/PerComW.2014.6815271

Detecting expectation-based spatio-temporal clusters formed during opportunistic sensing. / Orlinski, Matthew; Filer, Nick.
2014 IEEE International Conference on Pervasive Computing and Communication Workshops, PERCOM WORKSHOPS 2014|IEEE Int. Conf. Pervasive Comput. Commun. Workshops, PERCOM WORKSHOPS. IEEE Computer Society , 2014. p. 581-586.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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. © 2014 IEEE.

KW - data mining

KW - pattern clustering

KW - spatiotemporal phenomena

KW - statistical analysis

KW - clustering algorithms

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KW - spatiotemporal cluster detection

KW - spatiotemporal clustering technique

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Detecting expectation-based spatio-temporal clusters formed during opportunistic sensing

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Keywords

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